{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/language-modeling/papers/6","list_of":"/task/language-modeling","task":"Language Modeling","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":6,"pages_in_order":142,"rows_per_page":100,"rows":[501,600],"of":14182,"counts":{"archive_papers_tagged":14182,"with_a_code_link":5620,"where_syntology_ran_a_sample":1894,"not_listed_spam_title":0,"listed":14182,"listed_where_code_ran":1894,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1580,"every_run_a_failure_of_syntologys_instrument":314,"listed_with_a_run_with_no_instrument_failure":1580,"listed_every_run_a_failure_of_syntologys_instrument":314,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/language-modeling","prev":"/task/language-modeling/papers/5","next":"/task/language-modeling/papers/7","papers":[{"url":"/paper/regularized-adaptive-momentum-dual-averaging","slug":"regularized-adaptive-momentum-dual-averaging","title":"Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network","date":"2024-03-21","arxiv_id":"2403.14398","repositories_listed":2,"syntology":{"n":16,"n_ran":12,"n_constructed":1,"n_ran_checked":4,"n_instrument":8,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":16,"phrase":"12 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 8 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/regularized-adaptive-momentum-dual-averaging#ran","syntology_url":"https://syntology.ai/paper/2403.14398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14398"}},"official":{"repos":["ismoptgroup/ramda","ismoptgroup/ramda_exp"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rewardbench-evaluating-reward-models-for","slug":"rewardbench-evaluating-reward-models-for","title":"RewardBench: Evaluating Reward Models for Language Modeling","date":"2024-03-20","arxiv_id":"2403.13787","repositories_listed":2,"syntology":null},{"url":"/paper/embedded-named-entity-recognition-using","slug":"embedded-named-entity-recognition-using","title":"Embedded Named Entity Recognition using Probing Classifiers","date":"2024-03-18","arxiv_id":"2403.11747","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":2,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/embedded-named-entity-recognition-using#ran","syntology_url":"https://syntology.ai/paper/2403.11747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11747"}},"official":{"repos":["nicpopovic/stoke","nicpopovic/ember"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/training-a-small-emotional-vision-language","slug":"training-a-small-emotional-vision-language","title":"Training A Small Emotional Vision Language Model for Visual Art Comprehension","date":"2024-03-17","arxiv_id":"2403.11150","repositories_listed":2,"syntology":null},{"url":"/paper/videoagent-long-form-video-understanding-with","slug":"videoagent-long-form-video-understanding-with","title":"VideoAgent: Long-form Video Understanding with Large Language Model as Agent","date":"2024-03-15","arxiv_id":"2403.10517","repositories_listed":2,"syntology":null},{"url":"/paper/generative-pretrained-structured-transformers","slug":"generative-pretrained-structured-transformers","title":"Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale","date":"2024-03-13","arxiv_id":"2403.08293","repositories_listed":2,"syntology":{"n":7,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generative-pretrained-structured-transformers#ran","syntology_url":"https://syntology.ai/paper/2403.08293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08293"}},"official":{"repos":["ant-research/structuredlm_rtdt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/workarena-how-capable-are-web-agents-at","slug":"workarena-how-capable-are-web-agents-at","title":"WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?","date":"2024-03-12","arxiv_id":"2403.07718","repositories_listed":2,"syntology":null},{"url":"/paper/injecagent-benchmarking-indirect-prompt","slug":"injecagent-benchmarking-indirect-prompt","title":"InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents","date":"2024-03-05","arxiv_id":"2403.02691","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/injecagent-benchmarking-indirect-prompt#ran","syntology_url":"https://syntology.ai/paper/2403.02691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02691"}},"official":{"repos":["uiuc-kang-lab/injecagent"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-scale-protein-language-model-for","slug":"multi-scale-protein-language-model-for","title":"ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling","date":"2024-03-05","arxiv_id":"2403.12995","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-scale-protein-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2403.12995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12995"}},"official":{"repos":["zhengkangjie/esm-aa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/intactkv-improving-large-language-model","slug":"intactkv-improving-large-language-model","title":"IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact","date":"2024-03-02","arxiv_id":"2403.01241","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/intactkv-improving-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2403.01241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01241"}},"official":{"repos":["ruikangliu/IntactKV"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/archer-training-language-model-agents-via","slug":"archer-training-language-model-agents-via","title":"ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL","date":"2024-02-29","arxiv_id":"2402.19446","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/archer-training-language-model-agents-via#ran","syntology_url":"https://syntology.ai/paper/2402.19446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.19446"}},"official":{"repos":["yifeizhou02/archer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/rinalmo-general-purpose-rna-language-models","slug":"rinalmo-general-purpose-rna-language-models","title":"RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks","date":"2024-02-29","arxiv_id":"2403.00043","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rinalmo-general-purpose-rna-language-models#ran","syntology_url":"https://syntology.ai/paper/2403.00043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00043"}},"official":{"repos":["lbcb-sci/rinalmo","ml4bio/rna-fm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mathwell-generating-educational-math-word","slug":"mathwell-generating-educational-math-word","title":"MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations","date":"2024-02-24","arxiv_id":"2402.15861","repositories_listed":2,"syntology":null},{"url":"/paper/self-retrieval-building-an-information","slug":"self-retrieval-building-an-information","title":"Self-Retrieval: End-to-End Information Retrieval with One Large Language Model","date":"2024-02-23","arxiv_id":"2403.00801","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/self-retrieval-building-an-information#ran","syntology_url":"https://syntology.ai/paper/2403.00801","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.00801"}},"official":{"repos":["icip-cas/selfretrieval","tangqiaoyu/selfretrieval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/criticbench-evaluating-large-language-models","slug":"criticbench-evaluating-large-language-models","title":"CriticEval: Evaluating Large Language Model as Critic","date":"2024-02-21","arxiv_id":"2402.13764","repositories_listed":2,"syntology":null},{"url":"/paper/trap-targeted-random-adversarial-prompt","slug":"trap-targeted-random-adversarial-prompt","title":"TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification","date":"2024-02-20","arxiv_id":"2402.12991","repositories_listed":2,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/trap-targeted-random-adversarial-prompt#ran","syntology_url":"https://syntology.ai/paper/2402.12991","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.12991"}},"official":{"repos":["framartin/trap","parameterlab/trap"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/generation-meets-verification-accelerating","slug":"generation-meets-verification-accelerating","title":"Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding","date":"2024-02-19","arxiv_id":"2402.11809","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":4,"n_instrument":5,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/generation-meets-verification-accelerating#ran","syntology_url":"https://syntology.ai/paper/2402.11809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11809"}},"official":{"repos":["cteant/space","hiyouga/llama-factory"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/query-based-adversarial-prompt-generation","slug":"query-based-adversarial-prompt-generation","title":"Query-Based Adversarial Prompt Generation","date":"2024-02-19","arxiv_id":"2402.12329","repositories_listed":2,"syntology":null},{"url":"/paper/laco-large-language-model-pruning-via-layer","slug":"laco-large-language-model-pruning-via-layer","title":"LaCo: Large Language Model Pruning via Layer Collapse","date":"2024-02-17","arxiv_id":"2402.11187","repositories_listed":2,"syntology":null},{"url":"/paper/linear-transformers-with-learnable-kernel","slug":"linear-transformers-with-learnable-kernel","title":"Linear Transformers with Learnable Kernel Functions are Better In-Context Models","date":"2024-02-16","arxiv_id":"2402.10644","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/linear-transformers-with-learnable-kernel#ran","syntology_url":"https://syntology.ai/paper/2402.10644","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10644"}},"official":{"repos":["sustcsonglin/flash-linear-attention","corl-team/rebased"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-modal-preference-alignment-remedies","slug":"multi-modal-preference-alignment-remedies","title":"Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language Models","date":"2024-02-16","arxiv_id":"2402.10884","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-modal-preference-alignment-remedies#ran","syntology_url":"https://syntology.ai/paper/2402.10884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.10884"}},"official":{"repos":["findalexli/mllm-dpo"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/de-cop-detecting-copyrighted-content-in","slug":"de-cop-detecting-copyrighted-content-in","title":"DE-COP: Detecting Copyrighted Content in Language Models Training Data","date":"2024-02-15","arxiv_id":"2402.09910","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/de-cop-detecting-copyrighted-content-in#ran","syntology_url":"https://syntology.ai/paper/2402.09910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09910"}},"official":{"repos":["avduarte333/de-cop_method","leililab/de-cop"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-representational-instruction","slug":"generative-representational-instruction","title":"Generative Representational Instruction Tuning","date":"2024-02-15","arxiv_id":"2402.09906","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":5,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generative-representational-instruction#ran","syntology_url":"https://syntology.ai/paper/2402.09906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09906"}},"official":{"repos":["contextualai/gritlm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/automathtext-autonomous-data-selection-with","slug":"automathtext-autonomous-data-selection-with","title":"Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts","date":"2024-02-12","arxiv_id":"2402.07625","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/automathtext-autonomous-data-selection-with#ran","syntology_url":"https://syntology.ai/paper/2402.07625","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.07625"}},"official":{"repos":["hiyouga/llama-factory","yifanzhang-pro/automathtext"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/screenai-a-vision-language-model-for-ui-and","slug":"screenai-a-vision-language-model-for-ui-and","title":"ScreenAI: A Vision-Language Model for UI and Infographics Understanding","date":"2024-02-07","arxiv_id":"2402.04615","repositories_listed":2,"syntology":null},{"url":"/paper/can-mamba-learn-how-to-learn-a-comparative","slug":"can-mamba-learn-how-to-learn-a-comparative","title":"Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks","date":"2024-02-06","arxiv_id":"2402.04248","repositories_listed":2,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/can-mamba-learn-how-to-learn-a-comparative#ran","syntology_url":"https://syntology.ai/paper/2402.04248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.04248"}},"official":{"repos":["krafton-ai/mambaformer-icl"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/jailbreaking-attack-against-multimodal-large","slug":"jailbreaking-attack-against-multimodal-large","title":"Jailbreaking Attack against Multimodal Large Language Model","date":"2024-02-04","arxiv_id":"2402.02309","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/jailbreaking-attack-against-multimodal-large#ran","syntology_url":"https://syntology.ai/paper/2402.02309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02309"}},"official":{"repos":["abc03570128/jailbreaking-attack-against-multimodal-large-language-model"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/integrating-large-language-models-in-causal","slug":"integrating-large-language-models-in-causal","title":"Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach","date":"2024-02-02","arxiv_id":"2402.01454","repositories_listed":2,"syntology":null},{"url":"/paper/towards-efficient-and-exact-optimization-of","slug":"towards-efficient-and-exact-optimization-of","title":"Towards Efficient Exact Optimization of Language Model Alignment","date":"2024-02-01","arxiv_id":"2402.00856","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-efficient-and-exact-optimization-of#ran","syntology_url":"https://syntology.ai/paper/2402.00856","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.00856"}},"official":{"repos":["haozheji/exact-optimization"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lanegraph2seq-lane-topology-extraction-with","slug":"lanegraph2seq-lane-topology-extraction-with","title":"LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement","date":"2024-01-31","arxiv_id":"2401.17609","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lanegraph2seq-lane-topology-extraction-with#ran","syntology_url":"https://syntology.ai/paper/2401.17609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17609"}},"official":{"repos":["fudan-zvg/roadnet"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/arrows-of-time-for-large-language-models","slug":"arrows-of-time-for-large-language-models","title":"Arrows of Time for Large Language Models","date":"2024-01-30","arxiv_id":"2401.17505","repositories_listed":2,"syntology":null},{"url":"/paper/l-autoda-leveraging-large-language-models-for","slug":"l-autoda-leveraging-large-language-models-for","title":"L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks","date":"2024-01-27","arxiv_id":"2401.15335","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/l-autoda-leveraging-large-language-models-for#ran","syntology_url":"https://syntology.ai/paper/2401.15335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.15335"}},"official":{"repos":["pgg3/L-AutoDA"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/turna-a-turkish-encoder-decoder-language","slug":"turna-a-turkish-encoder-decoder-language","title":"TURNA: A Turkish Encoder-Decoder Language Model for Enhanced Understanding and Generation","date":"2024-01-25","arxiv_id":"2401.14373","repositories_listed":2,"syntology":null},{"url":"/paper/tool-lmm-a-large-multi-modal-model-for-tool","slug":"tool-lmm-a-large-multi-modal-model-for-tool","title":"MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning","date":"2024-01-19","arxiv_id":"2401.10727","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tool-lmm-a-large-multi-modal-model-for-tool#ran","syntology_url":"https://syntology.ai/paper/2401.10727","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10727"}},"official":{"repos":["mllm-tool/mllm-tool","tool-lmm/tool-lmm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unlocking-efficiency-in-large-language-model","slug":"unlocking-efficiency-in-large-language-model","title":"Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding","date":"2024-01-15","arxiv_id":"2401.07851","repositories_listed":2,"syntology":null},{"url":"/paper/evaluating-language-model-agency-through","slug":"evaluating-language-model-agency-through","title":"Evaluating Language Model Agency through Negotiations","date":"2024-01-09","arxiv_id":"2401.04536","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evaluating-language-model-agency-through#ran","syntology_url":"https://syntology.ai/paper/2401.04536","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04536"}},"official":{"repos":["epfl-dlab/lamen"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tinyllama-an-open-source-small-language-model","slug":"tinyllama-an-open-source-small-language-model","title":"TinyLlama: An Open-Source Small Language Model","date":"2024-01-04","arxiv_id":"2401.02385","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tinyllama-an-open-source-small-language-model#ran","syntology_url":"https://syntology.ai/paper/2401.02385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02385"}},"official":{"repos":["Lightning-AI/lit-gpt","jzhang38/tinyllama"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-mechanistic-understanding-of-alignment","slug":"a-mechanistic-understanding-of-alignment","title":"A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity","date":"2024-01-03","arxiv_id":"2401.01967","repositories_listed":2,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/a-mechanistic-understanding-of-alignment#ran","syntology_url":"https://syntology.ai/paper/2401.01967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.01967"}},"official":{"repos":["ajyl/dpo_toxic"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/differentially-private-low-rank-adaptation-of","slug":"differentially-private-low-rank-adaptation-of","title":"Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning","date":"2023-12-29","arxiv_id":"2312.17493","repositories_listed":2,"syntology":null},{"url":"/paper/challenge-llms-to-reason-about-reasoning-a","slug":"challenge-llms-to-reason-about-reasoning-a","title":"MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation","date":"2023-12-28","arxiv_id":"2312.17080","repositories_listed":2,"syntology":null},{"url":"/paper/tinygpt-v-efficient-multimodal-large-language","slug":"tinygpt-v-efficient-multimodal-large-language","title":"TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones","date":"2023-12-28","arxiv_id":"2312.16862","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tinygpt-v-efficient-multimodal-large-language#ran","syntology_url":"https://syntology.ai/paper/2312.16862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16862"}},"official":{"repos":["dlyuangod/tinygpt-v"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/solar-10-7b-scaling-large-language-models","slug":"solar-10-7b-scaling-large-language-models","title":"SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling","date":"2023-12-23","arxiv_id":"2312.15166","repositories_listed":2,"syntology":{"n":26,"n_ran":22,"n_constructed":0,"n_ran_checked":17,"n_instrument":5,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":3,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/solar-10-7b-scaling-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2312.15166","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.15166"}},"official":null}},{"url":"/paper/lingoqa-video-question-answering-for","slug":"lingoqa-video-question-answering-for","title":"LingoQA: Visual Question Answering for Autonomous Driving","date":"2023-12-21","arxiv_id":"2312.14115","repositories_listed":2,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lingoqa-video-question-answering-for#ran","syntology_url":"https://syntology.ai/paper/2312.14115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.14115"}},"official":{"repos":["wayveai/lingoqa"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/powerinfer-fast-large-language-model-serving","slug":"powerinfer-fast-large-language-model-serving","title":"PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU","date":"2023-12-16","arxiv_id":"2312.12456","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/powerinfer-fast-large-language-model-serving#ran","syntology_url":"https://syntology.ai/paper/2312.12456","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12456"}},"official":{"repos":["sjtu-ipads/powerinfer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/switchhead-accelerating-transformers-with","slug":"switchhead-accelerating-transformers-with","title":"SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention","date":"2023-12-13","arxiv_id":"2312.07987","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/switchhead-accelerating-transformers-with#ran","syntology_url":"https://syntology.ai/paper/2312.07987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07987"}},"official":{"repos":["robertcsordas/switchhead","robertcsordas/moe_attention"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficiently-programming-large-language-models","slug":"efficiently-programming-large-language-models","title":"SGLang: Efficient Execution of Structured Language Model Programs","date":"2023-12-12","arxiv_id":"2312.07104","repositories_listed":2,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":9,"n_instrument":7,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficiently-programming-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2312.07104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.07104"}},"official":{"repos":["sgl-project/sglang"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/localized-symbolic-knowledge-distillation-for-1","slug":"localized-symbolic-knowledge-distillation-for-1","title":"Localized Symbolic Knowledge Distillation for Visual Commonsense Models","date":"2023-12-08","arxiv_id":"2312.04837","repositories_listed":2,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/localized-symbolic-knowledge-distillation-for-1#ran","syntology_url":"https://syntology.ai/paper/2312.04837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.04837"}},"official":{"repos":["jamespark3922/localized-skd","jamespark3922/lskd"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/timechat-a-time-sensitive-multimodal-large","slug":"timechat-a-time-sensitive-multimodal-large","title":"TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding","date":"2023-12-04","arxiv_id":"2312.02051","repositories_listed":2,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/timechat-a-time-sensitive-multimodal-large#ran","syntology_url":"https://syntology.ai/paper/2312.02051","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02051"}},"official":{"repos":["renshuhuai-andy/timechat"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/dolphins-multimodal-language-model-for","slug":"dolphins-multimodal-language-model-for","title":"Dolphins: Multimodal Language Model for Driving","date":"2023-12-01","arxiv_id":"2312.00438","repositories_listed":2,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/dolphins-multimodal-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2312.00438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.00438"}},"official":null}},{"url":"/paper/esm-nbr-fast-and-accurate-nucleic-acid","slug":"esm-nbr-fast-and-accurate-nucleic-acid","title":"ESM-NBR: fast and accurate nucleic acid-binding residue prediction via protein language model feature representation and multi-task learning","date":"2023-12-01","arxiv_id":"2312.00842","repositories_listed":2,"syntology":null},{"url":"/paper/acoustic-prompt-tuning-empowering-large","slug":"acoustic-prompt-tuning-empowering-large","title":"Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities","date":"2023-11-30","arxiv_id":"2312.00249","repositories_listed":2,"syntology":null},{"url":"/paper/critiquellm-scaling-llm-as-critic-for","slug":"critiquellm-scaling-llm-as-critic-for","title":"CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation","date":"2023-11-30","arxiv_id":"2311.18702","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":3,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/critiquellm-scaling-llm-as-critic-for#ran","syntology_url":"https://syntology.ai/paper/2311.18702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18702"}},"official":{"repos":["thu-coai/critiquellm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/taiwan-llm-bridging-the-linguistic-divide","slug":"taiwan-llm-bridging-the-linguistic-divide","title":"Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model","date":"2023-11-29","arxiv_id":"2311.17487","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/taiwan-llm-bridging-the-linguistic-divide#ran","syntology_url":"https://syntology.ai/paper/2311.17487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17487"}},"official":{"repos":["miulab/taiwan-llama","miulab/taiwan-llm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/turkishbertweet-fast-and-reliable-large","slug":"turkishbertweet-fast-and-reliable-large","title":"TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis","date":"2023-11-29","arxiv_id":"2311.18063","repositories_listed":2,"syntology":null},{"url":"/paper/yuan-2-0-a-large-language-model-with","slug":"yuan-2-0-a-large-language-model-with","title":"YUAN 2.0: A Large Language Model with Localized Filtering-based Attention","date":"2023-11-27","arxiv_id":"2311.15786","repositories_listed":2,"syntology":null},{"url":"/paper/language-model-inversion","slug":"language-model-inversion","title":"Language Model Inversion","date":"2023-11-22","arxiv_id":"2311.13647","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/language-model-inversion#ran","syntology_url":"https://syntology.ai/paper/2311.13647","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13647"}},"official":{"repos":["jxmorris12/vec2text"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/extracting-definienda-in-mathematical","slug":"extracting-definienda-in-mathematical","title":"Extracting Definienda in Mathematical Scholarly Articles with Transformers","date":"2023-11-21","arxiv_id":"2311.12448","repositories_listed":2,"syntology":null},{"url":"/paper/towards-natural-language-guided-drones","slug":"towards-natural-language-guided-drones","title":"Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching","date":"2023-11-21","arxiv_id":"2311.12751","repositories_listed":2,"syntology":null},{"url":"/paper/leveraging-llms-for-synthesizing-training","slug":"leveraging-llms-for-synthesizing-training","title":"Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval","date":"2023-11-10","arxiv_id":"2311.05800","repositories_listed":2,"syntology":null},{"url":"/paper/deelm-dependency-enhanced-large-language","slug":"deelm-dependency-enhanced-large-language","title":"BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings","date":"2023-11-09","arxiv_id":"2311.05296","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deelm-dependency-enhanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2311.05296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05296"}},"official":{"repos":["4ai/bellm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/mplug-owl2-revolutionizing-multi-modal-large","slug":"mplug-owl2-revolutionizing-multi-modal-large","title":"mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration","date":"2023-11-07","arxiv_id":"2311.04257","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mplug-owl2-revolutionizing-multi-modal-large#ran","syntology_url":"https://syntology.ai/paper/2311.04257","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.04257"}},"official":{"repos":["x-plug/mplug-owl"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/large-language-model-can-interpret-latent","slug":"large-language-model-can-interpret-latent","title":"Large Language Model Can Interpret Latent Space of Sequential Recommender","date":"2023-10-31","arxiv_id":"2310.20487","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/large-language-model-can-interpret-latent#ran","syntology_url":"https://syntology.ai/paper/2310.20487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.20487"}},"official":{"repos":["yangzhengyi98/recinterpreter"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/zephyr-direct-distillation-of-lm-alignment","slug":"zephyr-direct-distillation-of-lm-alignment","title":"Zephyr: Direct Distillation of LM Alignment","date":"2023-10-25","arxiv_id":"2310.16944","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/zephyr-direct-distillation-of-lm-alignment#ran","syntology_url":"https://syntology.ai/paper/2310.16944","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16944"}},"official":null}},{"url":"/paper/lorashear-efficient-large-language-model","slug":"lorashear-efficient-large-language-model","title":"LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery","date":"2023-10-24","arxiv_id":"2310.18356","repositories_listed":2,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lorashear-efficient-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2310.18356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18356"}},"official":null}},{"url":"/paper/rtsum-relation-triple-based-interpretable","slug":"rtsum-relation-triple-based-interpretable","title":"RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization","date":"2023-10-21","arxiv_id":"2310.13895","repositories_listed":2,"syntology":null},{"url":"/paper/bitnet-scaling-1-bit-transformers-for-large","slug":"bitnet-scaling-1-bit-transformers-for-large","title":"BitNet: Scaling 1-bit Transformers for Large Language Models","date":"2023-10-17","arxiv_id":"2310.11453","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bitnet-scaling-1-bit-transformers-for-large#ran","syntology_url":"https://syntology.ai/paper/2310.11453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11453"}},"official":null}},{"url":"/paper/large-language-model-unlearning","slug":"large-language-model-unlearning","title":"Large Language Model Unlearning","date":"2023-10-14","arxiv_id":"2310.10683","repositories_listed":2,"syntology":null},{"url":"/paper/minigpt-v2-large-language-model-as-a-unified","slug":"minigpt-v2-large-language-model-as-a-unified","title":"MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning","date":"2023-10-14","arxiv_id":"2310.09478","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/minigpt-v2-large-language-model-as-a-unified#ran","syntology_url":"https://syntology.ai/paper/2310.09478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09478"}},"official":null}},{"url":"/paper/reward-augmented-decoding-efficient","slug":"reward-augmented-decoding-efficient","title":"Reward-Augmented Decoding: Efficient Controlled Text Generation With a Unidirectional Reward Model","date":"2023-10-14","arxiv_id":"2310.09520","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/reward-augmented-decoding-efficient#ran","syntology_url":"https://syntology.ai/paper/2310.09520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09520"}},"official":{"repos":["haikangdeng/RAD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-detoxifying-language-models-via","slug":"self-detoxifying-language-models-via","title":"Self-Detoxifying Language Models via Toxification Reversal","date":"2023-10-14","arxiv_id":"2310.09573","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/self-detoxifying-language-models-via#ran","syntology_url":"https://syntology.ai/paper/2310.09573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09573"}},"official":{"repos":["cooperleong00/toxificationreversal"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/puoberta-training-and-evaluation-of-a-curated","slug":"puoberta-training-and-evaluation-of-a-curated","title":"PuoBERTa: Training and evaluation of a curated language model for Setswana","date":"2023-10-13","arxiv_id":"2310.09141","repositories_listed":2,"syntology":null},{"url":"/paper/cachegen-fast-context-loading-for-language","slug":"cachegen-fast-context-loading-for-language","title":"CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving","date":"2023-10-11","arxiv_id":"2310.07240","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cachegen-fast-context-loading-for-language#ran","syntology_url":"https://syntology.ai/paper/2310.07240","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07240"}},"official":{"repos":["uchi-jcl/cachegen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dipmark-a-stealthy-efficient-and-resilient","slug":"dipmark-a-stealthy-efficient-and-resilient","title":"A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models","date":"2023-10-11","arxiv_id":"2310.07710","repositories_listed":2,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dipmark-a-stealthy-efficient-and-resilient#ran","syntology_url":"https://syntology.ai/paper/2310.07710","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07710"}},"official":{"repos":["yihwu/dipmark"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ferret-refer-and-ground-anything-anywhere-at","slug":"ferret-refer-and-ground-anything-anywhere-at","title":"Ferret: Refer and Ground Anything Anywhere at Any Granularity","date":"2023-10-11","arxiv_id":"2310.07704","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ferret-refer-and-ground-anything-anywhere-at#ran","syntology_url":"https://syntology.ai/paper/2310.07704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07704"}},"official":{"repos":["apple/ml-ferret"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sheared-llama-accelerating-language-model-pre","slug":"sheared-llama-accelerating-language-model-pre","title":"Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning","date":"2023-10-10","arxiv_id":"2310.06694","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sheared-llama-accelerating-language-model-pre#ran","syntology_url":"https://syntology.ai/paper/2310.06694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06694"}},"official":{"repos":["princeton-nlp/llm-shearing"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/recomp-improving-retrieval-augmented-lms-with","slug":"recomp-improving-retrieval-augmented-lms-with","title":"RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation","date":"2023-10-06","arxiv_id":"2310.04408","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/recomp-improving-retrieval-augmented-lms-with#ran","syntology_url":"https://syntology.ai/paper/2310.04408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.04408"}},"official":{"repos":["carriex/recomp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-one-preference-for-all-multi-objective","slug":"beyond-one-preference-for-all-multi-objective","title":"Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference Optimization","date":"2023-10-05","arxiv_id":"2310.03708","repositories_listed":2,"syntology":null},{"url":"/paper/cat-lm-training-language-models-on-aligned","slug":"cat-lm-training-language-models-on-aligned","title":"CAT-LM: Training Language Models on Aligned Code And Tests","date":"2023-10-02","arxiv_id":"2310.01602","repositories_listed":2,"syntology":null},{"url":"/paper/l2mac-large-language-model-automatic-computer","slug":"l2mac-large-language-model-automatic-computer","title":"L2MAC: Large Language Model Automatic Computer for Extensive Code Generation","date":"2023-10-02","arxiv_id":"2310.02003","repositories_listed":2,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/l2mac-large-language-model-automatic-computer#ran","syntology_url":"https://syntology.ai/paper/2310.02003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.02003"}},"official":{"repos":["samholt/l2mac","vanderschaarlab/l2mac"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/qwen-technical-report","slug":"qwen-technical-report","title":"Qwen Technical Report","date":"2023-09-28","arxiv_id":"2309.16609","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/qwen-technical-report#ran","syntology_url":"https://syntology.ai/paper/2309.16609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16609"}},"official":{"repos":["QwenLM/Qwen-7B","qwenlm/qwen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rllte-long-term-evolution-project-of","slug":"rllte-long-term-evolution-project-of","title":"RLLTE: Long-Term Evolution Project of Reinforcement Learning","date":"2023-09-28","arxiv_id":"2309.16382","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rllte-long-term-evolution-project-of#ran","syntology_url":"https://syntology.ai/paper/2309.16382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16382"}},"official":{"repos":["RLE-Foundation/rllte"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/effective-long-context-scaling-of-foundation","slug":"effective-long-context-scaling-of-foundation","title":"Effective Long-Context Scaling of Foundation Models","date":"2023-09-27","arxiv_id":"2309.16039","repositories_listed":2,"syntology":null},{"url":"/paper/efficient-post-training-quantization-with-fp8","slug":"efficient-post-training-quantization-with-fp8","title":"Efficient Post-training Quantization with FP8 Formats","date":"2023-09-26","arxiv_id":"2309.14592","repositories_listed":2,"syntology":null},{"url":"/paper/deepspeed-visualchat-multi-round-multi-image","slug":"deepspeed-visualchat-multi-round-multi-image","title":"DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention","date":"2023-09-25","arxiv_id":"2309.14327","repositories_listed":2,"syntology":null},{"url":"/paper/speaker-attribution-in-german-parliamentary","slug":"speaker-attribution-in-german-parliamentary","title":"Speaker attribution in German parliamentary debates with QLoRA-adapted large language models","date":"2023-09-18","arxiv_id":"2309.09902","repositories_listed":2,"syntology":null},{"url":"/paper/fedjudge-federated-legal-large-language-model","slug":"fedjudge-federated-legal-large-language-model","title":"FedJudge: Federated Legal Large Language Model","date":"2023-09-15","arxiv_id":"2309.08173","repositories_listed":2,"syntology":null},{"url":"/paper/frustratingly-simple-memory-efficiency-for","slug":"frustratingly-simple-memory-efficiency-for","title":"Vocabulary-level Memory Efficiency for Language Model Fine-tuning","date":"2023-09-15","arxiv_id":"2309.08708","repositories_listed":2,"syntology":null},{"url":"/paper/mmicl-empowering-vision-language-model-with","slug":"mmicl-empowering-vision-language-model-with","title":"MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning","date":"2023-09-14","arxiv_id":"2309.07915","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mmicl-empowering-vision-language-model-with#ran","syntology_url":"https://syntology.ai/paper/2309.07915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07915"}},"official":{"repos":["haozhezhao/mic","pkunlp-icler/mic"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-the-efficacy-of-supervised","slug":"evaluating-the-efficacy-of-supervised","title":"Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media","date":"2023-09-07","arxiv_id":"2309.03564","repositories_listed":2,"syntology":null},{"url":"/paper/detecting-language-model-attacks-with","slug":"detecting-language-model-attacks-with","title":"Detecting Language Model Attacks with Perplexity","date":"2023-08-27","arxiv_id":"2308.14132","repositories_listed":2,"syntology":null},{"url":"/paper/isr-llm-iterative-self-refined-large-language","slug":"isr-llm-iterative-self-refined-large-language","title":"ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning","date":"2023-08-26","arxiv_id":"2308.13724","repositories_listed":2,"syntology":null},{"url":"/paper/scieval-a-multi-level-large-language-model","slug":"scieval-a-multi-level-large-language-model","title":"SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research","date":"2023-08-25","arxiv_id":"2308.13149","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scieval-a-multi-level-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2308.13149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13149"}},"official":{"repos":["opendfm/bai-scieval","opendfm/scieval"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/qwen-vl-a-frontier-large-vision-language","slug":"qwen-vl-a-frontier-large-vision-language","title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","date":"2023-08-24","arxiv_id":"2308.12966","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/qwen-vl-a-frontier-large-vision-language#ran","syntology_url":"https://syntology.ai/paper/2308.12966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12966"}},"official":{"repos":["qwenlm/qwen-vl"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/large-multilingual-models-pivot-zero-shot","slug":"large-multilingual-models-pivot-zero-shot","title":"Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages","date":"2023-08-23","arxiv_id":"2308.12038","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/large-multilingual-models-pivot-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2308.12038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12038"}},"official":{"repos":["openbmb/viscpm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-on-large-language-model-based","slug":"a-survey-on-large-language-model-based","title":"A Survey on Large Language Model based Autonomous Agents","date":"2023-08-22","arxiv_id":"2308.11432","repositories_listed":2,"syntology":null},{"url":"/paper/activation-addition-steering-language-models","slug":"activation-addition-steering-language-models","title":"Steering Language Models With Activation Engineering","date":"2023-08-20","arxiv_id":"2308.10248","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/activation-addition-steering-language-models#ran","syntology_url":"https://syntology.ai/paper/2308.10248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10248"}},"official":{"repos":["montemac/activation_additions"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/chat-3d-data-efficiently-tuning-large","slug":"chat-3d-data-efficiently-tuning-large","title":"Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes","date":"2023-08-17","arxiv_id":"2308.08769","repositories_listed":2,"syntology":null},{"url":"/paper/chinese-spelling-correction-as-rephrasing","slug":"chinese-spelling-correction-as-rephrasing","title":"Chinese Spelling Correction as Rephrasing Language Model","date":"2023-08-17","arxiv_id":"2308.08796","repositories_listed":2,"syntology":null},{"url":"/paper/pro-cap-leveraging-a-frozen-vision-language","slug":"pro-cap-leveraging-a-frozen-vision-language","title":"Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection","date":"2023-08-16","arxiv_id":"2308.08088","repositories_listed":2,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/pro-cap-leveraging-a-frozen-vision-language#ran","syntology_url":"https://syntology.ai/paper/2308.08088","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08088"}},"official":{"repos":["social-ai-studio/pro-cap"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/fly-swat-or-cannon-cost-effective-language","slug":"fly-swat-or-cannon-cost-effective-language","title":"Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling","date":"2023-08-11","arxiv_id":"2308.06077","repositories_listed":2,"syntology":null}],"record_sha256":"9f1a2e2db02aa0640be8aebd32e01c4dd1acea0e5edbd3a9ec2c62f47bc4baf8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}