{"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/large-language-model/papers/19","list_of":"/task/large-language-model","task":"Large Language Model","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":19,"pages_in_order":61,"rows_per_page":100,"rows":[1801,1900],"of":6097,"counts":{"archive_papers_tagged":6097,"with_a_code_link":2250,"where_syntology_ran_a_sample":801,"not_listed_spam_title":0,"listed":6097,"listed_where_code_ran":801,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":683,"every_run_a_failure_of_syntologys_instrument":118,"listed_with_a_run_with_no_instrument_failure":683,"listed_every_run_a_failure_of_syntologys_instrument":118,"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/large-language-model","prev":"/task/large-language-model/papers/18","next":"/task/large-language-model/papers/20","papers":[{"url":"/paper/weakly-supervised-detection-of-hallucinations","slug":"weakly-supervised-detection-of-hallucinations","title":"Weakly Supervised Detection of Hallucinations in LLM Activations","date":"2023-12-05","arxiv_id":"2312.02798","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/weakly-supervised-detection-of-hallucinations#ran","syntology_url":"https://syntology.ai/paper/2312.02798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02798"}},"official":{"repos":["Trusted-AI/adversarial-robustness-toolbox"],"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":["official"]}}},{"url":"/paper/characterizing-large-language-model-geometry","slug":"characterizing-large-language-model-geometry","title":"Characterizing Large Language Model Geometry Helps Solve Toxicity Detection and Generation","date":"2023-12-04","arxiv_id":"2312.01648","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":13,"phrase":"7 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; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/characterizing-large-language-model-geometry#ran","syntology_url":"https://syntology.ai/paper/2312.01648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01648"}},"official":{"repos":["randallbalestriero/splinellm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-dependencies-in-fact-editing-for","slug":"evaluating-dependencies-in-fact-editing-for","title":"Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness","date":"2023-12-04","arxiv_id":"2312.01858","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/evaluating-dependencies-in-fact-editing-for#ran","syntology_url":"https://syntology.ai/paper/2312.01858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01858"}},"official":{"repos":["mcgill-nlp/logicalknowedit"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/exchange-of-thought-enhancing-large-language","slug":"exchange-of-thought-enhancing-large-language","title":"Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication","date":"2023-12-04","arxiv_id":"2312.01823","repositories_listed":1,"syntology":null},{"url":"/paper/instructta-instruction-tuned-targeted-attack","slug":"instructta-instruction-tuned-targeted-attack","title":"InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models","date":"2023-12-04","arxiv_id":"2312.01886","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":12,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/instructta-instruction-tuned-targeted-attack#ran","syntology_url":"https://syntology.ai/paper/2312.01886","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01886"}},"official":{"repos":["xunguangwang/instructta"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-models-as-consistent-story","slug":"large-language-models-as-consistent-story","title":"StoryGPT-V: Large Language Models as Consistent Story Visualizers","date":"2023-12-04","arxiv_id":"2312.02252","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"9 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; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/large-language-models-as-consistent-story#ran","syntology_url":"https://syntology.ai/paper/2312.02252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02252"}},"official":{"repos":["xiaoqian-shen/StoryGPT-V"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/bootstrapping-interactive-image-text","slug":"bootstrapping-interactive-image-text","title":"Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image Captioning","date":"2023-12-02","arxiv_id":"2312.01191","repositories_listed":1,"syntology":null},{"url":"/paper/conceptual-engineering-using-large-language","slug":"conceptual-engineering-using-large-language","title":"Conceptual Engineering Using Large Language Models","date":"2023-12-01","arxiv_id":"2312.03749","repositories_listed":1,"syntology":null},{"url":"/paper/arthmodel-enhance-arithmetic-skills-to-large","slug":"arthmodel-enhance-arithmetic-skills-to-large","title":"ArthModel: Enhance Arithmetic Skills to Large Language Model","date":"2023-11-30","arxiv_id":"2311.18609","repositories_listed":1,"syntology":null},{"url":"/paper/covid-19-vaccine-misinformation-in-middle","slug":"covid-19-vaccine-misinformation-in-middle","title":"COVID-19 Vaccine Misinformation in Middle Income Countries","date":"2023-11-30","arxiv_id":"2311.18195","repositories_listed":1,"syntology":null},{"url":"/paper/mplug-paperowl-scientific-diagram-analysis","slug":"mplug-paperowl-scientific-diagram-analysis","title":"mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model","date":"2023-11-30","arxiv_id":"2311.18248","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/mplug-paperowl-scientific-diagram-analysis#ran","syntology_url":"https://syntology.ai/paper/2311.18248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18248"}},"official":{"repos":["x-plug/mplug-docowl"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/ost-refining-text-knowledge-with-optimal","slug":"ost-refining-text-knowledge-with-optimal","title":"OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition","date":"2023-11-30","arxiv_id":"2312.00096","repositories_listed":1,"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":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) · 1 unverified","sample_list":"/paper/ost-refining-text-knowledge-with-optimal#ran","syntology_url":"https://syntology.ai/paper/2312.00096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.00096"}},"official":{"repos":["tomchen-ctj/OST"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/radialog-a-large-vision-language-model-for","slug":"radialog-a-large-vision-language-model-for","title":"RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance","date":"2023-11-30","arxiv_id":"2311.18681","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 2 unverified","sample_list":"/paper/radialog-a-large-vision-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2311.18681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.18681"}},"official":{"repos":["chantalmp/radialog"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/chatillusion-efficient-aligning-interleaved","slug":"chatillusion-efficient-aligning-interleaved","title":"M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation","date":"2023-11-29","arxiv_id":"2311.17963","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-vision-language-alignment-makes","slug":"contrastive-vision-language-alignment-makes","title":"Contrastive Vision-Language Alignment Makes Efficient Instruction Learner","date":"2023-11-29","arxiv_id":"2311.17945","repositories_listed":1,"syntology":null},{"url":"/paper/chatgpt-s-one-year-anniversary-are-open","slug":"chatgpt-s-one-year-anniversary-are-open","title":"ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?","date":"2023-11-28","arxiv_id":"2311.16989","repositories_listed":1,"syntology":null},{"url":"/paper/war-and-peace-waragent-large-language-model","slug":"war-and-peace-waragent-large-language-model","title":"War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars","date":"2023-11-28","arxiv_id":"2311.17227","repositories_listed":1,"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":0,"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/war-and-peace-waragent-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2311.17227","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17227"}},"official":{"repos":["agiresearch/waragent"],"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/compositional-chain-of-thought-prompting-for","slug":"compositional-chain-of-thought-prompting-for","title":"Compositional Chain-of-Thought Prompting for Large Multimodal Models","date":"2023-11-27","arxiv_id":"2311.17076","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/compositional-chain-of-thought-prompting-for#ran","syntology_url":"https://syntology.ai/paper/2311.17076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17076"}},"official":{"repos":["chancharikmitra/ccot"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dp-opt-make-large-language-model-your-privacy","slug":"dp-opt-make-large-language-model-your-privacy","title":"DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer","date":"2023-11-27","arxiv_id":"2312.03724","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dp-opt-make-large-language-model-your-privacy#ran","syntology_url":"https://syntology.ai/paper/2312.03724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.03724"}},"official":{"repos":["vita-group/dp-opt"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/intercontrol-generate-human-motion","slug":"intercontrol-generate-human-motion","title":"InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint","date":"2023-11-27","arxiv_id":"2311.15864","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":8,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/intercontrol-generate-human-motion#ran","syntology_url":"https://syntology.ai/paper/2311.15864","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.15864"}},"official":{"repos":["zhenzhiwang/intercontrol"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/llmga-multimodal-large-language-model-based","slug":"llmga-multimodal-large-language-model-based","title":"LLMGA: Multimodal Large Language Model based Generation Assistant","date":"2023-11-27","arxiv_id":"2311.16500","repositories_listed":1,"syntology":null},{"url":"/paper/removing-nsfw-concepts-from-vision-and","slug":"removing-nsfw-concepts-from-vision-and","title":"Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models","date":"2023-11-27","arxiv_id":"2311.16254","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/removing-nsfw-concepts-from-vision-and#ran","syntology_url":"https://syntology.ai/paper/2311.16254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16254"}},"official":{"repos":["aimagelab/safe-clip"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ufin-universal-feature-interaction-network","slug":"ufin-universal-feature-interaction-network","title":"UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction","date":"2023-11-27","arxiv_id":"2311.15493","repositories_listed":1,"syntology":null},{"url":"/paper/vtrain-a-simulation-framework-for-evaluating","slug":"vtrain-a-simulation-framework-for-evaluating","title":"vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training","date":"2023-11-27","arxiv_id":"2312.12391","repositories_listed":1,"syntology":null},{"url":"/paper/paragraph-to-image-generation-with","slug":"paragraph-to-image-generation-with","title":"Paragraph-to-Image Generation with Information-Enriched Diffusion Model","date":"2023-11-24","arxiv_id":"2311.14284","repositories_listed":1,"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/paragraph-to-image-generation-with#ran","syntology_url":"https://syntology.ai/paper/2311.14284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14284"}},"official":{"repos":["weijiawu/paradiffusion"],"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/when-is-off-policy-evaluation-useful-a-data","slug":"when-is-off-policy-evaluation-useful-a-data","title":"When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective","date":"2023-11-23","arxiv_id":"2311.14110","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/when-is-off-policy-evaluation-useful-a-data#ran","syntology_url":"https://syntology.ai/paper/2311.14110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14110"}},"official":{"repos":["holarissun/Data-Centric-OPE"],"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/as-llm-when-algorithm-selection-meets-large","slug":"as-llm-when-algorithm-selection-meets-large","title":"Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation","date":"2023-11-22","arxiv_id":"2311.13184","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-is-a-good-policy-teacher","slug":"large-language-model-is-a-good-policy-teacher","title":"Large Language Model as a Policy Teacher for Training Reinforcement Learning Agents","date":"2023-11-22","arxiv_id":"2311.13373","repositories_listed":1,"syntology":null},{"url":"/paper/soulstyler-using-large-language-model-to","slug":"soulstyler-using-large-language-model-to","title":"Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object","date":"2023-11-22","arxiv_id":"2311.13562","repositories_listed":1,"syntology":null},{"url":"/paper/towards-improving-document-understanding-an","slug":"towards-improving-document-understanding-an","title":"Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs","date":"2023-11-22","arxiv_id":"2311.13194","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-improving-document-understanding-an#ran","syntology_url":"https://syntology.ai/paper/2311.13194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13194"}},"official":{"repos":["harrytea/tgdoc"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/vamos-versatile-action-models-for-video","slug":"vamos-versatile-action-models-for-video","title":"Vamos: Versatile Action Models for Video Understanding","date":"2023-11-22","arxiv_id":"2311.13627","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vamos-versatile-action-models-for-video#ran","syntology_url":"https://syntology.ai/paper/2311.13627","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13627"}},"official":{"repos":["brown-palm/Vamos"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-scene-graph-generation-with","slug":"enhancing-scene-graph-generation-with","title":"Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge","date":"2023-11-21","arxiv_id":"2311.12889","repositories_listed":1,"syntology":null},{"url":"/paper/oasis-data-curation-and-assessment-system-for","slug":"oasis-data-curation-and-assessment-system-for","title":"Oasis: Data Curation and Assessment System for Pretraining of Large Language Models","date":"2023-11-21","arxiv_id":"2311.12537","repositories_listed":1,"syntology":null},{"url":"/paper/causal-structure-learning-supervised-by-large","slug":"causal-structure-learning-supervised-by-large","title":"Causal Structure Learning Supervised by Large Language Model","date":"2023-11-20","arxiv_id":"2311.11689","repositories_listed":1,"syntology":null},{"url":"/paper/how-well-chatgpt-understand-malaysian-english","slug":"how-well-chatgpt-understand-malaysian-english","title":"How well ChatGPT understand Malaysian English? An Evaluation on Named Entity Recognition and Relation Extraction","date":"2023-11-20","arxiv_id":"2311.11583","repositories_listed":1,"syntology":null},{"url":"/paper/lion-empowering-multimodal-large-language","slug":"lion-empowering-multimodal-large-language","title":"LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge","date":"2023-11-20","arxiv_id":"2311.11860","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/lion-empowering-multimodal-large-language#ran","syntology_url":"https://syntology.ai/paper/2311.11860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11860"}},"official":{"repos":["rshaojimmy/jiutian"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/taiyi-a-bilingual-fine-tuned-large-language","slug":"taiyi-a-bilingual-fine-tuned-large-language","title":"Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks","date":"2023-11-20","arxiv_id":"2311.11608","repositories_listed":1,"syntology":null},{"url":"/paper/geosam-fine-tuning-sam-with-sparse-and-dense","slug":"geosam-fine-tuning-sam-with-sparse-and-dense","title":"GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation","date":"2023-11-19","arxiv_id":"2311.11319","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-and-retrieving-generalizable","slug":"distilling-and-retrieving-generalizable","title":"Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections","date":"2023-11-17","arxiv_id":"2311.10678","repositories_listed":1,"syntology":null},{"url":"/paper/language-generation-from-human-brain","slug":"language-generation-from-human-brain","title":"Language Generation from Brain Recordings","date":"2023-11-16","arxiv_id":"2311.09889","repositories_listed":1,"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":3,"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/language-generation-from-human-brain#ran","syntology_url":"https://syntology.ai/paper/2311.09889","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09889"}},"official":{"repos":["yeziyi1998/brain-language-generation"],"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/leveraging-llms-in-scholarly-knowledge-graph","slug":"leveraging-llms-in-scholarly-knowledge-graph","title":"Leveraging LLMs in Scholarly Knowledge Graph Question Answering","date":"2023-11-16","arxiv_id":"2311.09841","repositories_listed":1,"syntology":null},{"url":"/paper/improving-fit-to-human-reading-times-via","slug":"improving-fit-to-human-reading-times-via","title":"Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the \"right reasons\"?","date":"2023-11-15","arxiv_id":"2311.09325","repositories_listed":1,"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":5,"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/improving-fit-to-human-reading-times-via#ran","syntology_url":"https://syntology.ai/paper/2311.09325","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09325"}},"official":{"repos":["TongLiu-github/TemperatureSaling4RTs"],"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/multistage-collaborative-knowledge","slug":"multistage-collaborative-knowledge","title":"Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation","date":"2023-11-15","arxiv_id":"2311.08640","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":1,"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: 2 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multistage-collaborative-knowledge#ran","syntology_url":"https://syntology.ai/paper/2311.08640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08640"}},"official":{"repos":["andotalao24/multistage-collaborative-knowledge-distillation"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/psyeval-a-comprehensive-large-language-model","slug":"psyeval-a-comprehensive-large-language-model","title":"PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language Models","date":"2023-11-15","arxiv_id":"2311.09189","repositories_listed":1,"syntology":null},{"url":"/paper/videocon-robust-video-language-alignment-via","slug":"videocon-robust-video-language-alignment-via","title":"VideoCon: Robust Video-Language Alignment via Contrast Captions","date":"2023-11-15","arxiv_id":"2311.10111","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/videocon-robust-video-language-alignment-via#ran","syntology_url":"https://syntology.ai/paper/2311.10111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.10111"}},"official":{"repos":["hritikbansal/videocon"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/acid-abstractive-content-based-ids-for","slug":"acid-abstractive-content-based-ids-for","title":"Summarization-Based Document IDs for Generative Retrieval with Language Models","date":"2023-11-14","arxiv_id":"2311.08593","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/acid-abstractive-content-based-ids-for#ran","syntology_url":"https://syntology.ai/paper/2311.08593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08593"}},"official":{"repos":["lihaoxin2020/summarization-based-document-ids-for-generative-retrieval"],"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":["official"]}}},{"url":"/paper/automated-title-and-abstract-screening-for","slug":"automated-title-and-abstract-screening-for","title":"Automated title and abstract screening for scoping reviews using the GPT-4 Large Language Model","date":"2023-11-14","arxiv_id":"2311.07918","repositories_listed":1,"syntology":null},{"url":"/paper/how-you-prompt-matters-even-task-oriented","slug":"how-you-prompt-matters-even-task-oriented","title":"How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text Detection","date":"2023-11-14","arxiv_id":"2311.08369","repositories_listed":1,"syntology":null},{"url":"/paper/llatrieval-llm-verified-retrieval-for","slug":"llatrieval-llm-verified-retrieval-for","title":"LLatrieval: LLM-Verified Retrieval for Verifiable Generation","date":"2023-11-14","arxiv_id":"2311.07838","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"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) · 3 unverified","sample_list":"/paper/llatrieval-llm-verified-retrieval-for#ran","syntology_url":"https://syntology.ai/paper/2311.07838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07838"}},"official":{"repos":["beastyz/llm-verified-retrieval"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/magic-benchmarking-large-language-model","slug":"magic-benchmarking-large-language-model","title":"MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration","date":"2023-11-14","arxiv_id":"2311.08562","repositories_listed":1,"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":0,"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/magic-benchmarking-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2311.08562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08562"}},"official":{"repos":["cathyxl/magic"],"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/mechagents-large-language-model-multi-agent","slug":"mechagents-large-language-model-multi-agent","title":"MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge","date":"2023-11-14","arxiv_id":"2311.08166","repositories_listed":1,"syntology":null},{"url":"/paper/towards-open-ended-visual-recognition-with","slug":"towards-open-ended-visual-recognition-with","title":"Towards Open-Ended Visual Recognition with Large Language Model","date":"2023-11-14","arxiv_id":"2311.08400","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/towards-open-ended-visual-recognition-with#ran","syntology_url":"https://syntology.ai/paper/2311.08400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08400"}},"official":{"repos":["bytedance/omniscient-model"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-audio-captioning-with-audio","slug":"zero-shot-audio-captioning-with-audio","title":"Zero-shot audio captioning with audio-language model guidance and audio context keywords","date":"2023-11-14","arxiv_id":"2311.08396","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":17,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/zero-shot-audio-captioning-with-audio#ran","syntology_url":"https://syntology.ai/paper/2311.08396","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08396"}},"official":{"repos":["explainableml/zeraucap"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-automatic-data-enhancement-for-document","slug":"semi-automatic-data-enhancement-for-document","title":"Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models","date":"2023-11-13","arxiv_id":"2311.07314","repositories_listed":1,"syntology":null},{"url":"/paper/sphinx-the-joint-mixing-of-weights-tasks-and","slug":"sphinx-the-joint-mixing-of-weights-tasks-and","title":"SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models","date":"2023-11-13","arxiv_id":"2311.07575","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sphinx-the-joint-mixing-of-weights-tasks-and#ran","syntology_url":"https://syntology.ai/paper/2311.07575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07575"}},"official":{"repos":["alpha-vllm/llama2-accessory"],"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/to-tell-the-truth-language-of-deception-and","slug":"to-tell-the-truth-language-of-deception-and","title":"To Tell The Truth: Language of Deception and Language Models","date":"2023-11-13","arxiv_id":"2311.07092","repositories_listed":1,"syntology":null},{"url":"/paper/tunable-soft-prompts-are-messengers-in","slug":"tunable-soft-prompts-are-messengers-in","title":"Tunable Soft Prompts are Messengers in Federated Learning","date":"2023-11-12","arxiv_id":"2311.06805","repositories_listed":1,"syntology":null},{"url":"/paper/cfbenchmark-chinese-financial-assistant","slug":"cfbenchmark-chinese-financial-assistant","title":"CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model","date":"2023-11-10","arxiv_id":"2311.05812","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/cfbenchmark-chinese-financial-assistant#ran","syntology_url":"https://syntology.ai/paper/2311.05812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05812"}},"official":{"repos":["tongjifinlab/cfbenchmark"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/chimed-gpt-a-chinese-medical-large-language","slug":"chimed-gpt-a-chinese-medical-large-language","title":"ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences","date":"2023-11-10","arxiv_id":"2311.06025","repositories_listed":1,"syntology":null},{"url":"/paper/cloudeval-yaml-a-practical-benchmark-for","slug":"cloudeval-yaml-a-practical-benchmark-for","title":"CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation","date":"2023-11-10","arxiv_id":"2401.06786","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-large-language-models-using-skill","slug":"distilling-large-language-models-using-skill","title":"Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks","date":"2023-11-10","arxiv_id":"2311.06383","repositories_listed":1,"syntology":null},{"url":"/paper/follow-up-differential-descriptions-language","slug":"follow-up-differential-descriptions-language","title":"Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification","date":"2023-11-10","arxiv_id":"2311.07593","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/follow-up-differential-descriptions-language#ran","syntology_url":"https://syntology.ai/paper/2311.07593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07593"}},"official":{"repos":["batsresearch/fudd"],"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":["official"]}}},{"url":"/paper/heaps-law-in-gpt-neo-large-language-model","slug":"heaps-law-in-gpt-neo-large-language-model","title":"Heaps' Law in GPT-Neo Large Language Model Emulated Corpora","date":"2023-11-10","arxiv_id":"2311.06377","repositories_listed":1,"syntology":null},{"url":"/paper/chain-of-images-for-intuitively-reasoning","slug":"chain-of-images-for-intuitively-reasoning","title":"Chain of Images for Intuitively Reasoning","date":"2023-11-09","arxiv_id":"2311.09241","repositories_listed":1,"syntology":null},{"url":"/paper/deep-natural-language-feature-learning-for","slug":"deep-natural-language-feature-learning-for","title":"Deep Natural Language Feature Learning for Interpretable Prediction","date":"2023-11-09","arxiv_id":"2311.05754","repositories_listed":1,"syntology":null},{"url":"/paper/u-llava-unifying-multi-modal-tasks-via-large","slug":"u-llava-unifying-multi-modal-tasks-via-large","title":"u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model","date":"2023-11-09","arxiv_id":"2311.05348","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/u-llava-unifying-multi-modal-tasks-via-large#ran","syntology_url":"https://syntology.ai/paper/2311.05348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05348"}},"official":{"repos":["OPPOMKLab/u-LLaVA"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/zero-shot-translation-of-attention-patterns","slug":"zero-shot-translation-of-attention-patterns","title":"Zero-shot Translation of Attention Patterns in VQA Models to Natural Language","date":"2023-11-08","arxiv_id":"2311.05043","repositories_listed":1,"syntology":null},{"url":"/paper/aspects-of-human-memory-and-large-language","slug":"aspects-of-human-memory-and-large-language","title":"Aspects of human memory and Large Language Models","date":"2023-11-07","arxiv_id":"2311.03839","repositories_listed":1,"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/aspects-of-human-memory-and-large-language#ran","syntology_url":"https://syntology.ai/paper/2311.03839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03839"}},"official":{"repos":["rmldj/memory-llm-paper"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/conversations-in-galician-a-large-language","slug":"conversations-in-galician-a-large-language","title":"Conversations in Galician: a Large Language Model for an Underrepresented Language","date":"2023-11-07","arxiv_id":"2311.03812","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-based-long-tail-query","slug":"large-language-model-based-long-tail-query","title":"Large Language Model based Long-tail Query Rewriting in Taobao Search","date":"2023-11-07","arxiv_id":"2311.03758","repositories_listed":1,"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":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) · 3 unverified","sample_list":"/paper/large-language-model-based-long-tail-query#ran","syntology_url":"https://syntology.ai/paper/2311.03758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03758"}},"official":null}},{"url":"/paper/locating-cross-task-sequence-continuation","slug":"locating-cross-task-sequence-continuation","title":"Towards Interpretable Sequence Continuation: Analyzing Shared Circuits in Large Language Models","date":"2023-11-07","arxiv_id":"2311.04131","repositories_listed":1,"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/locating-cross-task-sequence-continuation#ran","syntology_url":"https://syntology.ai/paper/2311.04131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.04131"}},"official":{"repos":["apartresearch/seqcont_circuits"],"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/unified-low-resource-sequence-labeling-by","slug":"unified-low-resource-sequence-labeling-by","title":"Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning","date":"2023-11-07","arxiv_id":"2311.03748","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unified-low-resource-sequence-labeling-by#ran","syntology_url":"https://syntology.ai/paper/2311.03748","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03748"}},"official":{"repos":["psunlpgroup/fish-dip"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/alympics-language-agents-meet-game-theory","slug":"alympics-language-agents-meet-game-theory","title":"ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents","date":"2023-11-06","arxiv_id":"2311.03220","repositories_listed":1,"syntology":null},{"url":"/paper/deepinception-hypnotize-large-language-model","slug":"deepinception-hypnotize-large-language-model","title":"DeepInception: Hypnotize Large Language Model to Be Jailbreaker","date":"2023-11-06","arxiv_id":"2311.03191","repositories_listed":1,"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":0,"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/deepinception-hypnotize-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2311.03191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03191"}},"official":{"repos":["tmlr-group/deepinception"],"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/assessing-the-promise-and-pitfalls-of-chatgpt","slug":"assessing-the-promise-and-pitfalls-of-chatgpt","title":"Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation","date":"2023-11-05","arxiv_id":"2311.02640","repositories_listed":1,"syntology":null},{"url":"/paper/llm-enhanced-self-training-for-cross-domain","slug":"llm-enhanced-self-training-for-cross-domain","title":"LLM-enhanced Self-training for Cross-domain Constituency Parsing","date":"2023-11-05","arxiv_id":"2311.02660","repositories_listed":1,"syntology":null},{"url":"/paper/emojilm-modeling-the-new-emoji-language","slug":"emojilm-modeling-the-new-emoji-language","title":"EmojiLM: Modeling the New Emoji Language","date":"2023-11-03","arxiv_id":"2311.01751","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-large-language-model-for","slug":"collaborative-large-language-model-for","title":"Collaborative Large Language Model for Recommender Systems","date":"2023-11-02","arxiv_id":"2311.01343","repositories_listed":1,"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":0,"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/collaborative-large-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2311.01343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01343"}},"official":{"repos":["yaochenzhu/llm4rec"],"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/effective-human-ai-teams-via-learned-natural-1","slug":"effective-human-ai-teams-via-learned-natural-1","title":"Effective Human-AI Teams via Learned Natural Language Rules and Onboarding","date":"2023-11-02","arxiv_id":"2311.01007","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":12,"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) · 4 unverified","sample_list":"/paper/effective-human-ai-teams-via-learned-natural-1#ran","syntology_url":"https://syntology.ai/paper/2311.01007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01007"}},"official":{"repos":["clinicalml/onboarding_human_ai"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/comparing-optimization-targets-for-contrast","slug":"comparing-optimization-targets-for-contrast","title":"Comparing Optimization Targets for Contrast-Consistent Search","date":"2023-11-01","arxiv_id":"2311.00488","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/comparing-optimization-targets-for-contrast#ran","syntology_url":"https://syntology.ai/paper/2311.00488","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00488"}},"official":{"repos":["ash-ai-safety-hub/g3-nandi"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/improving-interpersonal-communication-by","slug":"improving-interpersonal-communication-by","title":"Improving Interpersonal Communication by Simulating Audiences with Language Models","date":"2023-11-01","arxiv_id":"2311.00687","repositories_listed":1,"syntology":null},{"url":"/paper/plug-and-play-policy-planner-for-large","slug":"plug-and-play-policy-planner-for-large","title":"Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents","date":"2023-11-01","arxiv_id":"2311.00262","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"8 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/plug-and-play-policy-planner-for-large#ran","syntology_url":"https://syntology.ai/paper/2311.00262","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00262"}},"official":{"repos":["dengyang17/ppdpp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["community","official"]}}},{"url":"/paper/interpretable-by-design-text-classification","slug":"interpretable-by-design-text-classification","title":"Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck","date":"2023-10-30","arxiv_id":"2310.19660","repositories_listed":1,"syntology":null},{"url":"/paper/mill-mutual-verification-with-large-language","slug":"mill-mutual-verification-with-large-language","title":"MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion","date":"2023-10-29","arxiv_id":"2310.19056","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-chatgpt-for-medical-applications","slug":"multimodal-chatgpt-for-medical-applications","title":"Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V","date":"2023-10-29","arxiv_id":"2310.19061","repositories_listed":1,"syntology":null},{"url":"/paper/foundation-models-meet-imbalanced-single-cell","slug":"foundation-models-meet-imbalanced-single-cell","title":"Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations","date":"2023-10-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/petailor-improving-large-language-model-by","slug":"petailor-improving-large-language-model-by","title":"Benchingmaking Large Langage Models in Biomedical Triple Extraction","date":"2023-10-27","arxiv_id":"2310.18463","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-animation-generation-and-control-on","slug":"real-time-animation-generation-and-control-on","title":"Real-time Animation Generation and Control on Rigged Models via Large Language Models","date":"2023-10-27","arxiv_id":"2310.17838","repositories_listed":1,"syntology":null},{"url":"/paper/an-open-source-data-contamination-report-for","slug":"an-open-source-data-contamination-report-for","title":"An Open Source Data Contamination Report for Large Language Models","date":"2023-10-26","arxiv_id":"2310.17589","repositories_listed":1,"syntology":null},{"url":"/paper/competeai-understanding-the-competition","slug":"competeai-understanding-the-competition","title":"CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents","date":"2023-10-26","arxiv_id":"2310.17512","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/competeai-understanding-the-competition#ran","syntology_url":"https://syntology.ai/paper/2310.17512","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17512"}},"official":{"repos":["microsoft/competeai"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/instoptima-evolutionary-multi-objective","slug":"instoptima-evolutionary-multi-objective","title":"InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction Operators","date":"2023-10-26","arxiv_id":"2310.17630","repositories_listed":1,"syntology":null},{"url":"/paper/the-impact-of-using-an-ai-chatbot-to-respond","slug":"the-impact-of-using-an-ai-chatbot-to-respond","title":"The impact of responding to patient messages with large language model assistance","date":"2023-10-26","arxiv_id":"2310.17703","repositories_listed":1,"syntology":null},{"url":"/paper/conditionally-combining-robot-skills-using","slug":"conditionally-combining-robot-skills-using","title":"Conditionally Combining Robot Skills using Large Language Models","date":"2023-10-25","arxiv_id":"2310.17019","repositories_listed":1,"syntology":null},{"url":"/paper/skymath-technical-report","slug":"skymath-technical-report","title":"SkyMath: Technical Report","date":"2023-10-25","arxiv_id":"2310.16713","repositories_listed":1,"syntology":null},{"url":"/paper/clinfo-ai-an-open-source-retrieval-augmented","slug":"clinfo-ai-an-open-source-retrieval-augmented","title":"Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature","date":"2023-10-24","arxiv_id":"2310.16146","repositories_listed":1,"syntology":null},{"url":"/paper/crash-clustering-removing-and-sharing-enhance","slug":"crash-clustering-removing-and-sharing-enhance","title":"CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language Model","date":"2023-10-24","arxiv_id":"2310.15477","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/crash-clustering-removing-and-sharing-enhance#ran","syntology_url":"https://syntology.ai/paper/2310.15477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15477"}},"official":{"repos":["tsinghuac3i/crash"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-tokens-efficient-llms-through","slug":"mixture-of-tokens-efficient-llms-through","title":"Mixture of Tokens: Continuous MoE through Cross-Example Aggregation","date":"2023-10-24","arxiv_id":"2310.15961","repositories_listed":1,"syntology":null},{"url":"/paper/conversational-recommender-system-and-large","slug":"conversational-recommender-system-and-large","title":"Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue","date":"2023-10-23","arxiv_id":"2310.14626","repositories_listed":1,"syntology":null},{"url":"/paper/disc-finllm-a-chinese-financial-large","slug":"disc-finllm-a-chinese-financial-large","title":"DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning","date":"2023-10-23","arxiv_id":"2310.15205","repositories_listed":1,"syntology":null},{"url":"/paper/llm-in-the-loop-leveraging-large-language","slug":"llm-in-the-loop-leveraging-large-language","title":"LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis","date":"2023-10-23","arxiv_id":"2310.15100","repositories_listed":1,"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/llm-in-the-loop-leveraging-large-language#ran","syntology_url":"https://syntology.ai/paper/2310.15100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15100"}},"official":{"repos":["sjdai/llm-thematic-analysis"],"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"]}}}],"record_sha256":"f35242d84187b26f424cfdfd335762006c513bd6d17d5dc1f5bc17fc097871a1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}