{"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/38","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":38,"pages_in_order":142,"rows_per_page":100,"rows":[3701,3800],"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/37","next":"/task/language-modeling/papers/39","papers":[{"url":"/paper/lmbot-distilling-graph-knowledge-into","slug":"lmbot-distilling-graph-knowledge-into","title":"LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection","date":"2023-06-30","arxiv_id":"2306.17408","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-system-for-systematic-generalization","slug":"a-hybrid-system-for-systematic-generalization","title":"A Hybrid System for Systematic Generalization in Simple Arithmetic Problems","date":"2023-06-29","arxiv_id":"2306.17249","repositories_listed":1,"syntology":null},{"url":"/paper/lyricwhiz-robust-multilingual-zero-shot","slug":"lyricwhiz-robust-multilingual-zero-shot","title":"LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT","date":"2023-06-29","arxiv_id":"2306.17103","repositories_listed":1,"syntology":null},{"url":"/paper/towards-personalized-cold-start","slug":"towards-personalized-cold-start","title":"Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations","date":"2023-06-29","arxiv_id":"2306.17256","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-personalized-cold-start#ran","syntology_url":"https://syntology.ai/paper/2306.17256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.17256"}},"official":{"repos":["jacksonwuxs/promptrec"],"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/chatlaw-open-source-legal-large-language","slug":"chatlaw-open-source-legal-large-language","title":"Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model","date":"2023-06-28","arxiv_id":"2306.16092","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-dialogue-generation-via-dynamic","slug":"enhancing-dialogue-generation-via-dynamic","title":"Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation","date":"2023-06-28","arxiv_id":"2306.16195","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-as-attributed-training-1","slug":"large-language-model-as-attributed-training-1","title":"Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias","date":"2023-06-28","arxiv_id":"2306.15895","repositories_listed":1,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":8,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"12 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; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/large-language-model-as-attributed-training-1#ran","syntology_url":"https://syntology.ai/paper/2306.15895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.15895"}},"official":{"repos":["yueyu1030/attrprompt"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/palm-predicting-actions-through-language","slug":"palm-predicting-actions-through-language","title":"Palm: Predicting Actions through Language Models @ Ego4D Long-Term Action Anticipation Challenge 2023","date":"2023-06-28","arxiv_id":"2306.16545","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"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) · 2 unverified","sample_list":"/paper/palm-predicting-actions-through-language#ran","syntology_url":"https://syntology.ai/paper/2306.16545","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16545"}},"official":{"repos":["dandoge/palm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/protein-dna-binding-sites-prediction-based-on","slug":"protein-dna-binding-sites-prediction-based-on","title":"Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning","date":"2023-06-28","arxiv_id":"2306.15912","repositories_listed":1,"syntology":null},{"url":"/paper/s2snet-a-pretrained-neural-network-for","slug":"s2snet-a-pretrained-neural-network-for","title":"S2SNet: A Pretrained Neural Network for Superconductivity Discovery","date":"2023-06-28","arxiv_id":"2306.16270","repositories_listed":1,"syntology":null},{"url":"/paper/towards-language-models-that-can-see-computer","slug":"towards-language-models-that-can-see-computer","title":"Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language","date":"2023-06-28","arxiv_id":"2306.16410","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-language-models-that-can-see-computer#ran","syntology_url":"https://syntology.ai/paper/2306.16410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16410"}},"official":{"repos":["contextualai/lens"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fauno-the-italian-large-language-model-that","slug":"fauno-the-italian-large-language-model-that","title":"Fauno: The Italian Large Language Model that will leave you senza parole!","date":"2023-06-26","arxiv_id":"2306.14457","repositories_listed":1,"syntology":null},{"url":"/paper/longcoder-a-long-range-pre-trained-language","slug":"longcoder-a-long-range-pre-trained-language","title":"LongCoder: A Long-Range Pre-trained Language Model for Code Completion","date":"2023-06-26","arxiv_id":"2306.14893","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":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/longcoder-a-long-range-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2306.14893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14893"}},"official":{"repos":["microsoft/CodeBERT"],"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/ontology-enrichment-from-texts-a-biomedical","slug":"ontology-enrichment-from-texts-a-biomedical","title":"Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement","date":"2023-06-26","arxiv_id":"2306.14704","repositories_listed":1,"syntology":null},{"url":"/paper/desco-learning-object-recognition-with-rich","slug":"desco-learning-object-recognition-with-rich","title":"DesCo: Learning Object Recognition with Rich Language Descriptions","date":"2023-06-24","arxiv_id":"2306.14060","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":4,"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/desco-learning-object-recognition-with-rich#ran","syntology_url":"https://syntology.ai/paper/2306.14060","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14060"}},"official":null}},{"url":"/paper/ualberta-at-semeval-2023-task-1-context","slug":"ualberta-at-semeval-2023-task-1-context","title":"UAlberta at SemEval-2023 Task 1: Context Augmentation and Translation for Multilingual Visual Word Sense Disambiguation","date":"2023-06-24","arxiv_id":"2306.14067","repositories_listed":1,"syntology":null},{"url":"/paper/bring-your-own-data-self-supervised","slug":"bring-your-own-data-self-supervised","title":"Bring Your Own Data! Self-Supervised Evaluation for Large Language Models","date":"2023-06-23","arxiv_id":"2306.13651","repositories_listed":1,"syntology":{"n":15,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":11,"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) · 11 unverified","sample_list":"/paper/bring-your-own-data-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2306.13651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13651"}},"official":{"repos":["neelsjain/byod"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/implementing-contextual-biasing-in-gpu","slug":"implementing-contextual-biasing-in-gpu","title":"Implementing contextual biasing in GPU decoder for online ASR","date":"2023-06-23","arxiv_id":"2306.15685","repositories_listed":1,"syntology":null},{"url":"/paper/long-range-language-modeling-with-self","slug":"long-range-language-modeling-with-self","title":"Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrieval","date":"2023-06-23","arxiv_id":"2306.13421","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/long-range-language-modeling-with-self#ran","syntology_url":"https://syntology.ai/paper/2306.13421","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13421"}},"official":{"repos":["ohadrubin/rpt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/product-information-extraction-using-chatgpt","slug":"product-information-extraction-using-chatgpt","title":"Product Information Extraction using ChatGPT","date":"2023-06-23","arxiv_id":"2306.14921","repositories_listed":1,"syntology":null},{"url":"/paper/system-level-natural-language-feedback","slug":"system-level-natural-language-feedback","title":"System-Level Natural Language Feedback","date":"2023-06-23","arxiv_id":"2306.13588","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/system-level-natural-language-feedback#ran","syntology_url":"https://syntology.ai/paper/2306.13588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13588"}},"official":{"repos":["yyy-apple/sys-nl-feedback"],"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/generative-multimodal-entity-linking","slug":"generative-multimodal-entity-linking","title":"Generative Multimodal Entity Linking","date":"2023-06-22","arxiv_id":"2306.12725","repositories_listed":1,"syntology":null},{"url":"/paper/mapping-and-cleaning-open-commonsense","slug":"mapping-and-cleaning-open-commonsense","title":"Mapping and Cleaning Open Commonsense Knowledge Bases with Generative Translation","date":"2023-06-22","arxiv_id":"2306.12766","repositories_listed":1,"syntology":null},{"url":"/paper/a-reference-less-quality-metric-for-automatic","slug":"a-reference-less-quality-metric-for-automatic","title":"A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision","date":"2023-06-21","arxiv_id":"2306.13114","repositories_listed":1,"syntology":null},{"url":"/paper/mass-producing-failures-of-multimodal-systems-1","slug":"mass-producing-failures-of-multimodal-systems-1","title":"Mass-Producing Failures of Multimodal Systems with Language Models","date":"2023-06-21","arxiv_id":"2306.12105","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":1,"n_no_contract":0,"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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mass-producing-failures-of-multimodal-systems-1#ran","syntology_url":"https://syntology.ai/paper/2306.12105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12105"}},"official":{"repos":["tsb0601/multimon"],"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/norefer-a-referenceless-quality-metric-for","slug":"norefer-a-referenceless-quality-metric-for","title":"NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning","date":"2023-06-21","arxiv_id":"2306.12577","repositories_listed":1,"syntology":null},{"url":"/paper/ophglm-training-an-ophthalmology-large","slug":"ophglm-training-an-ophthalmology-large","title":"OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue","date":"2023-06-21","arxiv_id":"2306.12174","repositories_listed":1,"syntology":null},{"url":"/paper/no-wrong-turns-the-simple-geometry-of-neural","slug":"no-wrong-turns-the-simple-geometry-of-neural","title":"No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths","date":"2023-06-20","arxiv_id":"2306.11922","repositories_listed":1,"syntology":null},{"url":"/paper/rs5m-a-large-scale-vision-language-dataset","slug":"rs5m-a-large-scale-vision-language-dataset","title":"RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing","date":"2023-06-20","arxiv_id":"2306.11300","repositories_listed":1,"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":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) · 1 unverified","sample_list":"/paper/rs5m-a-large-scale-vision-language-dataset#ran","syntology_url":"https://syntology.ai/paper/2306.11300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11300"}},"official":{"repos":["om-ai-lab/rs5m"],"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/sparse-modular-activation-for-efficient-1","slug":"sparse-modular-activation-for-efficient-1","title":"Sparse Modular Activation for Efficient Sequence Modeling","date":"2023-06-19","arxiv_id":"2306.11197","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sparse-modular-activation-for-efficient-1#ran","syntology_url":"https://syntology.ai/paper/2306.11197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11197"}},"official":{"repos":["renll/seqboat"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/evolutionary-verbalizer-search-for-prompt","slug":"evolutionary-verbalizer-search-for-prompt","title":"Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification","date":"2023-06-18","arxiv_id":"2306.10514","repositories_listed":1,"syntology":null},{"url":"/paper/futuretod-teaching-future-knowledge-to-pre","slug":"futuretod-teaching-future-knowledge-to-pre","title":"FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue","date":"2023-06-17","arxiv_id":"2306.10315","repositories_listed":1,"syntology":null},{"url":"/paper/kest-kernel-distance-based-efficient-self","slug":"kest-kernel-distance-based-efficient-self","title":"KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation","date":"2023-06-17","arxiv_id":"2306.10414","repositories_listed":1,"syntology":null},{"url":"/paper/llmva-gebc-large-language-model-with-video","slug":"llmva-gebc-large-language-model-with-video","title":"LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning","date":"2023-06-17","arxiv_id":"2306.10354","repositories_listed":1,"syntology":null},{"url":"/paper/data-selection-for-fine-tuning-large-language","slug":"data-selection-for-fine-tuning-large-language","title":"Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values","date":"2023-06-16","arxiv_id":"2306.10165","repositories_listed":1,"syntology":null},{"url":"/paper/fall-e-a-foley-sound-synthesis-model-and","slug":"fall-e-a-foley-sound-synthesis-model-and","title":"FALL-E: A Foley Sound Synthesis Model and Strategies","date":"2023-06-16","arxiv_id":"2306.09807","repositories_listed":1,"syntology":null},{"url":"/paper/just-one-byte-per-gradient-a-note-on-low","slug":"just-one-byte-per-gradient-a-note-on-low","title":"Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared Randomness","date":"2023-06-16","arxiv_id":"2306.10015","repositories_listed":1,"syntology":null},{"url":"/paper/unlocking-the-potential-of-user-feedback","slug":"unlocking-the-potential-of-user-feedback","title":"Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System","date":"2023-06-16","arxiv_id":"2306.09821","repositories_listed":1,"syntology":null},{"url":"/paper/chessgpt-bridging-policy-learning-and-1","slug":"chessgpt-bridging-policy-learning-and-1","title":"ChessGPT: Bridging Policy Learning and Language Modeling","date":"2023-06-15","arxiv_id":"2306.09200","repositories_listed":1,"syntology":{"n":20,"n_ran":14,"n_constructed":8,"n_ran_checked":10,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":0,"phrase":"14 ran (of which 8 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/chessgpt-bridging-policy-learning-and-1#ran","syntology_url":"https://syntology.ai/paper/2306.09200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09200"}},"official":{"repos":["waterhorse1/chessgpt"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":8,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/macaw-llm-multi-modal-language-modeling-with","slug":"macaw-llm-multi-modal-language-modeling-with","title":"Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration","date":"2023-06-15","arxiv_id":"2306.09093","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":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/macaw-llm-multi-modal-language-modeling-with#ran","syntology_url":"https://syntology.ai/paper/2306.09093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09093"}},"official":{"repos":["lyuchenyang/macaw-llm"],"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/neural-models-for-factual-inconsistency","slug":"neural-models-for-factual-inconsistency","title":"Neural models for Factual Inconsistency Classification with Explanations","date":"2023-06-15","arxiv_id":"2306.08872","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-image-enhancement-featuring","slug":"personalized-image-enhancement-featuring","title":"Personalized Image Enhancement Featuring Masked Style Modeling","date":"2023-06-15","arxiv_id":"2306.09334","repositories_listed":1,"syntology":null},{"url":"/paper/pushing-the-limits-of-unsupervised-unit","slug":"pushing-the-limits-of-unsupervised-unit","title":"Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation","date":"2023-06-15","arxiv_id":"2306.08920","repositories_listed":1,"syntology":null},{"url":"/paper/generate-to-understand-for-representation","slug":"generate-to-understand-for-representation","title":"Generate to Understand for Representation","date":"2023-06-14","arxiv_id":"2306.10056","repositories_listed":1,"syntology":null},{"url":"/paper/revealing-the-structure-of-language-model","slug":"revealing-the-structure-of-language-model","title":"Revealing the structure of language model capabilities","date":"2023-06-14","arxiv_id":"2306.10062","repositories_listed":1,"syntology":null},{"url":"/paper/world-to-words-grounded-open-vocabulary","slug":"world-to-words-grounded-open-vocabulary","title":"World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models","date":"2023-06-14","arxiv_id":"2306.08685","repositories_listed":1,"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":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/world-to-words-grounded-open-vocabulary#ran","syntology_url":"https://syntology.ai/paper/2306.08685","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08685"}},"official":{"repos":["sled-group/world-to-words"],"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/int2-1-towards-fine-tunable-quantized-large","slug":"int2-1-towards-fine-tunable-quantized-large","title":"INT2.1: Towards Fine-Tunable Quantized Large Language Models with Error Correction through Low-Rank Adaptation","date":"2023-06-13","arxiv_id":"2306.08162","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/int2-1-towards-fine-tunable-quantized-large#ran","syntology_url":"https://syntology.ai/paper/2306.08162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08162"}},"official":{"repos":["stochasticai/xturing"],"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/nocola-the-norwegian-corpus-of-linguistic","slug":"nocola-the-norwegian-corpus-of-linguistic","title":"NoCoLA: The Norwegian Corpus of Linguistic Acceptability","date":"2023-06-13","arxiv_id":"2306.07790","repositories_listed":1,"syntology":null},{"url":"/paper/tokenization-with-factorized-subword-encoding","slug":"tokenization-with-factorized-subword-encoding","title":"Tokenization with Factorized Subword Encoding","date":"2023-06-13","arxiv_id":"2306.07764","repositories_listed":1,"syntology":null},{"url":"/paper/xraygpt-chest-radiographs-summarization-using","slug":"xraygpt-chest-radiographs-summarization-using","title":"XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models","date":"2023-06-13","arxiv_id":"2306.07971","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-ascent-post-training-enhances","slug":"gradient-ascent-post-training-enhances","title":"Gradient Ascent Post-training Enhances Language Model Generalization","date":"2023-06-12","arxiv_id":"2306.07052","repositories_listed":1,"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/gradient-ascent-post-training-enhances#ran","syntology_url":"https://syntology.ai/paper/2306.07052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07052"}},"official":{"repos":["kaist-lklab/gap"],"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/valley-video-assistant-with-large-language","slug":"valley-video-assistant-with-large-language","title":"Valley: Video Assistant with Large Language model Enhanced abilitY","date":"2023-06-12","arxiv_id":"2306.07207","repositories_listed":1,"syntology":null},{"url":"/paper/are-intermediate-layers-and-labels-really","slug":"are-intermediate-layers-and-labels-really","title":"Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method","date":"2023-06-11","arxiv_id":"2306.06625","repositories_listed":1,"syntology":null},{"url":"/paper/gkd-a-general-knowledge-distillation","slug":"gkd-a-general-knowledge-distillation","title":"GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model","date":"2023-06-11","arxiv_id":"2306.06629","repositories_listed":1,"syntology":null},{"url":"/paper/quert-continual-pre-training-of-language","slug":"quert-continual-pre-training-of-language","title":"QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search","date":"2023-06-11","arxiv_id":"2306.06707","repositories_listed":1,"syntology":null},{"url":"/paper/aladdin-zero-shot-hallucination-of-stylized","slug":"aladdin-zero-shot-hallucination-of-stylized","title":"Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions","date":"2023-06-09","arxiv_id":"2306.06212","repositories_listed":1,"syntology":null},{"url":"/paper/language-models-can-learn-exceptions-to","slug":"language-models-can-learn-exceptions-to","title":"Language Models Can Learn Exceptions to Syntactic Rules","date":"2023-06-09","arxiv_id":"2306.05969","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-are-semi-parametric-1","slug":"large-language-models-are-semi-parametric-1","title":"Large Language Models Are Semi-Parametric Reinforcement Learning Agents","date":"2023-06-09","arxiv_id":"2306.07929","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/large-language-models-are-semi-parametric-1#ran","syntology_url":"https://syntology.ai/paper/2306.07929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07929"}},"official":{"repos":["opendfm/rememberer"],"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/hexatagging-projective-dependency-parsing-as","slug":"hexatagging-projective-dependency-parsing-as","title":"Hexatagging: Projective Dependency Parsing as Tagging","date":"2023-06-08","arxiv_id":"2306.05477","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-foundation-language-model-for","slug":"learning-a-foundation-language-model-for","title":"K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization","date":"2023-06-08","arxiv_id":"2306.05064","repositories_listed":1,"syntology":{"n":9,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/learning-a-foundation-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2306.05064","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05064"}},"official":{"repos":["davendw49/k2"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/mixture-of-supernets-improving-weight-sharing","slug":"mixture-of-supernets-improving-weight-sharing","title":"Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts","date":"2023-06-08","arxiv_id":"2306.04845","repositories_listed":1,"syntology":null},{"url":"/paper/reta-llm-a-retrieval-augmented-large-language","slug":"reta-llm-a-retrieval-augmented-large-language","title":"RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit","date":"2023-06-08","arxiv_id":"2306.05212","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/reta-llm-a-retrieval-augmented-large-language#ran","syntology_url":"https://syntology.ai/paper/2306.05212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05212"}},"official":{"repos":["ruc-gsai/yulan-ir"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robot-task-planning-based-on-large-language","slug":"robot-task-planning-based-on-large-language","title":"Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures","date":"2023-06-08","arxiv_id":"2306.05171","repositories_listed":1,"syntology":null},{"url":"/paper/can-current-nli-systems-handle-german-word","slug":"can-current-nli-systems-handle-german-word","title":"Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairs","date":"2023-06-07","arxiv_id":"2306.04523","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-masked-auto-encoder-for-retrieval","slug":"contextual-masked-auto-encoder-for-retrieval","title":"Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems","date":"2023-06-07","arxiv_id":"2306.04357","repositories_listed":1,"syntology":null},{"url":"/paper/privately-generating-tabular-data-using","slug":"privately-generating-tabular-data-using","title":"Privately generating tabular data using language models","date":"2023-06-07","arxiv_id":"2306.04803","repositories_listed":1,"syntology":null},{"url":"/paper/inference-time-intervention-eliciting","slug":"inference-time-intervention-eliciting","title":"Inference-Time Intervention: Eliciting Truthful Answers from a Language Model","date":"2023-06-06","arxiv_id":"2306.03341","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/inference-time-intervention-eliciting#ran","syntology_url":"https://syntology.ai/paper/2306.03341","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03341"}},"official":{"repos":["likenneth/honest_llama"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-difference-of-bert-style-and-clip","slug":"on-the-difference-of-bert-style-and-clip","title":"On the Difference of BERT-style and CLIP-style Text Encoders","date":"2023-06-06","arxiv_id":"2306.03678","repositories_listed":1,"syntology":null},{"url":"/paper/q-how-to-specialize-large-vision-language-1","slug":"q-how-to-specialize-large-vision-language-1","title":"Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!","date":"2023-06-06","arxiv_id":"2306.03932","repositories_listed":1,"syntology":null},{"url":"/paper/tkdp-threefold-knowledge-enriched-deep-prompt","slug":"tkdp-threefold-knowledge-enriched-deep-prompt","title":"TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition","date":"2023-06-06","arxiv_id":"2306.03974","repositories_listed":1,"syntology":null},{"url":"/paper/autoscrum-automating-project-planning-using","slug":"autoscrum-automating-project-planning-using","title":"AutoScrum: Automating Project Planning Using Large Language Models","date":"2023-06-05","arxiv_id":"2306.03197","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-middle-trained-language-models","slug":"benchmarking-middle-trained-language-models","title":"Benchmarking Middle-Trained Language Models for Neural Search","date":"2023-06-05","arxiv_id":"2306.02867","repositories_listed":1,"syntology":null},{"url":"/paper/improving-conversational-recommendation-4","slug":"improving-conversational-recommendation-4","title":"Improving Conversational Recommendation Systems via Counterfactual Data Simulation","date":"2023-06-05","arxiv_id":"2306.02842","repositories_listed":1,"syntology":{"n":5,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 5 unverified","sample_list":"/paper/improving-conversational-recommendation-4#ran","syntology_url":"https://syntology.ai/paper/2306.02842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02842"}},"official":{"repos":["rucaibox/cfcrs"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"url":"/paper/recagent-a-novel-simulation-paradigm-for","slug":"recagent-a-novel-simulation-paradigm-for","title":"User Behavior Simulation with Large Language Model based Agents","date":"2023-06-05","arxiv_id":"2306.02552","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-and-verbalizing-academic-ideas-by","slug":"exploring-and-verbalizing-academic-ideas-by","title":"Exploring and Verbalizing Academic Ideas by Concept Co-occurrence","date":"2023-06-04","arxiv_id":"2306.02282","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-augmented-narrative","slug":"large-language-model-augmented-narrative","title":"Large Language Model Augmented Narrative Driven Recommendations","date":"2023-06-04","arxiv_id":"2306.02250","repositories_listed":1,"syntology":null},{"url":"/paper/an-evaluation-of-log-parsing-with-chatgpt","slug":"an-evaluation-of-log-parsing-with-chatgpt","title":"Log Parsing: How Far Can ChatGPT Go?","date":"2023-06-02","arxiv_id":"2306.01590","repositories_listed":1,"syntology":null},{"url":"/paper/chatgpt-for-zero-shot-dialogue-state-tracking","slug":"chatgpt-for-zero-shot-dialogue-state-tracking","title":"ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?","date":"2023-06-02","arxiv_id":"2306.01386","repositories_listed":1,"syntology":null},{"url":"/paper/fair-multilingual-vandalism-detection-system","slug":"fair-multilingual-vandalism-detection-system","title":"Fair multilingual vandalism detection system for Wikipedia","date":"2023-06-02","arxiv_id":"2306.01650","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-human-feedback-gives-better","slug":"fine-grained-human-feedback-gives-better","title":"Fine-Grained Human Feedback Gives Better Rewards for Language Model Training","date":"2023-06-02","arxiv_id":"2306.01693","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/fine-grained-human-feedback-gives-better#ran","syntology_url":"https://syntology.ai/paper/2306.01693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01693"}},"official":null}},{"url":"/paper/light-coreference-resolution-for-russian-with","slug":"light-coreference-resolution-for-russian-with","title":"Light Coreference Resolution for Russian with Hierarchical Discourse Features","date":"2023-06-02","arxiv_id":"2306.01465","repositories_listed":1,"syntology":null},{"url":"/paper/training-free-neural-architecture-search-for","slug":"training-free-neural-architecture-search-for","title":"Training-free Neural Architecture Search for RNNs and Transformers","date":"2023-06-01","arxiv_id":"2306.00288","repositories_listed":1,"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/training-free-neural-architecture-search-for#ran","syntology_url":"https://syntology.ai/paper/2306.00288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00288"}},"official":{"repos":["aaronserianni/training-free-nas"],"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/vocabulary-free-image-classification-1","slug":"vocabulary-free-image-classification-1","title":"Vocabulary-free Image Classification","date":"2023-06-01","arxiv_id":"2306.00917","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/vocabulary-free-image-classification-1#ran","syntology_url":"https://syntology.ai/paper/2306.00917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00917"}},"official":{"repos":["altndrr/vic"],"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/an-invariant-learning-characterization-of","slug":"an-invariant-learning-characterization-of","title":"An Invariant Learning Characterization of Controlled Text Generation","date":"2023-05-31","arxiv_id":"2306.00198","repositories_listed":1,"syntology":null},{"url":"/paper/idas-intent-discovery-with-abstractive","slug":"idas-intent-discovery-with-abstractive","title":"IDAS: Intent Discovery with Abstractive Summarization","date":"2023-05-31","arxiv_id":"2305.19783","repositories_listed":1,"syntology":null},{"url":"/paper/lmcap-few-shot-multilingual-image-captioning","slug":"lmcap-few-shot-multilingual-image-captioning","title":"LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting","date":"2023-05-31","arxiv_id":"2305.19821","repositories_listed":1,"syntology":null},{"url":"/paper/neuron-to-graph-interpreting-language-model","slug":"neuron-to-graph-interpreting-language-model","title":"Neuron to Graph: Interpreting Language Model Neurons at Scale","date":"2023-05-31","arxiv_id":"2305.19911","repositories_listed":1,"syntology":null},{"url":"/paper/speaking-the-language-of-your-listener","slug":"speaking-the-language-of-your-listener","title":"Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind","date":"2023-05-31","arxiv_id":"2305.19933","repositories_listed":1,"syntology":null},{"url":"/paper/adapterem-pre-trained-language-model","slug":"adapterem-pre-trained-language-model","title":"AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning","date":"2023-05-30","arxiv_id":"2305.18725","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-sufficiency-lower-bounds-for","slug":"empirical-sufficiency-lower-bounds-for","title":"Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic Structures","date":"2023-05-30","arxiv_id":"2305.18915","repositories_listed":1,"syntology":null},{"url":"/paper/gpt4tools-teaching-large-language-model-to","slug":"gpt4tools-teaching-large-language-model-to","title":"GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction","date":"2023-05-30","arxiv_id":"2305.18752","repositories_listed":1,"syntology":null},{"url":"/paper/likelihood-based-diffusion-language-models-1","slug":"likelihood-based-diffusion-language-models-1","title":"Likelihood-Based Diffusion Language Models","date":"2023-05-30","arxiv_id":"2305.18619","repositories_listed":1,"syntology":null},{"url":"/paper/preserving-pre-trained-features-helps","slug":"preserving-pre-trained-features-helps","title":"Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models","date":"2023-05-30","arxiv_id":"2305.19249","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/preserving-pre-trained-features-helps#ran","syntology_url":"https://syntology.ai/paper/2305.19249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19249"}},"official":{"repos":["thu-ml/lm-calibration"],"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/adapting-learned-sparse-retrieval-for-long","slug":"adapting-learned-sparse-retrieval-for-long","title":"Adapting Learned Sparse Retrieval for Long Documents","date":"2023-05-29","arxiv_id":"2305.18494","repositories_listed":1,"syntology":null},{"url":"/paper/do-language-models-know-when-they-re","slug":"do-language-models-know-when-they-re","title":"Do Language Models Know When They're Hallucinating References?","date":"2023-05-29","arxiv_id":"2305.18248","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/do-language-models-know-when-they-re#ran","syntology_url":"https://syntology.ai/paper/2305.18248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18248"}},"official":{"repos":["microsoft/hallucinated-references"],"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/test-time-training-on-nearest-neighbors-for","slug":"test-time-training-on-nearest-neighbors-for","title":"Test-Time Training on Nearest Neighbors for Large Language Models","date":"2023-05-29","arxiv_id":"2305.18466","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/test-time-training-on-nearest-neighbors-for#ran","syntology_url":"https://syntology.ai/paper/2305.18466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18466"}},"official":{"repos":["socialfoundations/tttlm"],"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/kosbi-a-dataset-for-mitigating-social-bias","slug":"kosbi-a-dataset-for-mitigating-social-bias","title":"KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application","date":"2023-05-28","arxiv_id":"2305.17701","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-masked-language-modeling-for","slug":"rethinking-masked-language-modeling-for","title":"Rethinking Masked Language Modeling for Chinese Spelling Correction","date":"2023-05-28","arxiv_id":"2305.17721","repositories_listed":1,"syntology":null},{"url":"/paper/improving-generalization-in-language-model","slug":"improving-generalization-in-language-model","title":"Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques","date":"2023-05-27","arxiv_id":"2305.17378","repositories_listed":1,"syntology":null},{"url":"/paper/query-efficient-black-box-red-teaming-via","slug":"query-efficient-black-box-red-teaming-via","title":"Query-Efficient Black-Box Red Teaming via Bayesian Optimization","date":"2023-05-27","arxiv_id":"2305.17444","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/query-efficient-black-box-red-teaming-via#ran","syntology_url":"https://syntology.ai/paper/2305.17444","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17444"}},"official":{"repos":["snu-mllab/bayesian-red-teaming"],"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"]}}}],"record_sha256":"341d60fc769e6f10999348c56a7cbbd23bdc5c81c4016be67afec7eabd6f5ac0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}