{"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-modelling/papers/45","list_of":"/task/language-modelling","task":"Language Modelling","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":45,"pages_in_order":177,"rows_per_page":100,"rows":[4401,4500],"of":17610,"counts":{"archive_papers_tagged":17610,"with_a_code_link":7012,"where_syntology_ran_a_sample":2428,"not_listed_spam_title":0,"listed":17610,"listed_where_code_ran":2428,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2027,"every_run_a_failure_of_syntologys_instrument":401,"listed_with_a_run_with_no_instrument_failure":2027,"listed_every_run_a_failure_of_syntologys_instrument":401,"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-modelling","prev":"/task/language-modelling/papers/44","next":"/task/language-modelling/papers/46","papers":[{"url":"/paper/adaplus-integrating-nesterov-momentum-and","slug":"adaplus-integrating-nesterov-momentum-and","title":"AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW Basis","date":"2023-09-05","arxiv_id":"2309.01966","repositories_listed":1,"syntology":null},{"url":"/paper/nanot5-a-pytorch-framework-for-pre-training","slug":"nanot5-a-pytorch-framework-for-pre-training","title":"nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources","date":"2023-09-05","arxiv_id":"2309.02373","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/nanot5-a-pytorch-framework-for-pre-training#ran","syntology_url":"https://syntology.ai/paper/2309.02373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.02373"}},"official":{"repos":["piotrnawrot/nanot5"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/scaling-autoregressive-multi-modal-models","slug":"scaling-autoregressive-multi-modal-models","title":"Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning","date":"2023-09-05","arxiv_id":"2309.02591","repositories_listed":1,"syntology":null},{"url":"/paper/are-emergent-abilities-in-large-language","slug":"are-emergent-abilities-in-large-language","title":"Are Emergent Abilities in Large Language Models just In-Context Learning?","date":"2023-09-04","arxiv_id":"2309.01809","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/are-emergent-abilities-in-large-language#ran","syntology_url":"https://syntology.ai/paper/2309.01809","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01809"}},"official":{"repos":["ukplab/on-emergence"],"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/devil-decoding-vision-features-into-language","slug":"devil-decoding-vision-features-into-language","title":"DeViL: Decoding Vision features into Language","date":"2023-09-04","arxiv_id":"2309.01617","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"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) · 2 unverified","sample_list":"/paper/devil-decoding-vision-features-into-language#ran","syntology_url":"https://syntology.ai/paper/2309.01617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01617"}},"official":{"repos":["ExplainableML/DeViL"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/prompting-or-fine-tuning-a-comparative-study","slug":"prompting-or-fine-tuning-a-comparative-study","title":"Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction","date":"2023-09-04","arxiv_id":"2309.01715","repositories_listed":1,"syntology":null},{"url":"/paper/generative-social-choice","slug":"generative-social-choice","title":"Generative Social Choice","date":"2023-09-03","arxiv_id":"2309.01291","repositories_listed":1,"syntology":null},{"url":"/paper/linktransformer-a-unified-package-for-record","slug":"linktransformer-a-unified-package-for-record","title":"LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models","date":"2023-09-02","arxiv_id":"2309.00789","repositories_listed":1,"syntology":null},{"url":"/paper/batchprompt-accomplish-more-with-less","slug":"batchprompt-accomplish-more-with-less","title":"BatchPrompt: Accomplish more with less","date":"2023-09-01","arxiv_id":"2309.00384","repositories_listed":1,"syntology":null},{"url":"/paper/image-hijacking-adversarial-images-can","slug":"image-hijacking-adversarial-images-can","title":"Image Hijacks: Adversarial Images can Control Generative Models at Runtime","date":"2023-09-01","arxiv_id":"2309.00236","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/image-hijacking-adversarial-images-can#ran","syntology_url":"https://syntology.ai/paper/2309.00236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00236"}},"official":{"repos":["euanong/image-hijacks"],"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/let-the-models-respond-interpreting-language","slug":"let-the-models-respond-interpreting-language","title":"Let the Models Respond: Interpreting Language Model Detoxification Through the Lens of Prompt Dependence","date":"2023-09-01","arxiv_id":"2309.00751","repositories_listed":1,"syntology":null},{"url":"/paper/publicly-shareable-clinical-large-language","slug":"publicly-shareable-clinical-large-language","title":"Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes","date":"2023-09-01","arxiv_id":"2309.00237","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/publicly-shareable-clinical-large-language#ran","syntology_url":"https://syntology.ai/paper/2309.00237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00237"}},"official":{"repos":["starmpcc/asclepius"],"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/accurate-prediction-of-antibody-function-and","slug":"accurate-prediction-of-antibody-function-and","title":"Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model","date":"2023-08-31","arxiv_id":"2308.16713","repositories_listed":1,"syntology":null},{"url":"/paper/repcodec-a-speech-representation-codec-for","slug":"repcodec-a-speech-representation-codec-for","title":"RepCodec: A Speech Representation Codec for Speech Tokenization","date":"2023-08-31","arxiv_id":"2309.00169","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":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/repcodec-a-speech-representation-codec-for#ran","syntology_url":"https://syntology.ai/paper/2309.00169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.00169"}},"official":{"repos":["mct10/repcodec"],"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/dtrocr-decoder-only-transformer-for-optical","slug":"dtrocr-decoder-only-transformer-for-optical","title":"DTrOCR: Decoder-only Transformer for Optical Character Recognition","date":"2023-08-30","arxiv_id":"2308.15996","repositories_listed":1,"syntology":null},{"url":"/paper/llasm-large-language-and-speech-model","slug":"llasm-large-language-and-speech-model","title":"LLaSM: Large Language and Speech Model","date":"2023-08-30","arxiv_id":"2308.15930","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":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) · 2 unverified","sample_list":"/paper/llasm-large-language-and-speech-model#ran","syntology_url":"https://syntology.ai/paper/2308.15930","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15930"}},"official":{"repos":["linksoul-ai/llasm"],"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/materials-informatics-transformer-a-language","slug":"materials-informatics-transformer-a-language","title":"Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction","date":"2023-08-30","arxiv_id":"2308.16259","repositories_listed":1,"syntology":null},{"url":"/paper/characterizing-learning-curves-during","slug":"characterizing-learning-curves-during","title":"Characterizing Learning Curves During Language Model Pre-Training: Learning, Forgetting, and Stability","date":"2023-08-29","arxiv_id":"2308.15419","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"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) · 3 unverified","sample_list":"/paper/characterizing-learning-curves-during#ran","syntology_url":"https://syntology.ai/paper/2308.15419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15419"}},"official":{"repos":["tylerachang/lm-learning-curves"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/empowering-llm-to-use-smartphone-for","slug":"empowering-llm-to-use-smartphone-for","title":"AutoDroid: LLM-powered Task Automation in Android","date":"2023-08-29","arxiv_id":"2308.15272","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/empowering-llm-to-use-smartphone-for#ran","syntology_url":"https://syntology.ai/paper/2308.15272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15272"}},"official":null}},{"url":"/paper/enhancing-psychological-counseling-with-large","slug":"enhancing-psychological-counseling-with-large","title":"Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals","date":"2023-08-29","arxiv_id":"2308.15192","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-task-semantic-decomposition-framework","slug":"a-multi-task-semantic-decomposition-framework","title":"A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER","date":"2023-08-28","arxiv_id":"2308.14533","repositories_listed":1,"syntology":null},{"url":"/paper/covr-learning-composed-video-retrieval-from","slug":"covr-learning-composed-video-retrieval-from","title":"CoVR-2: Automatic Data Construction for Composed Video Retrieval","date":"2023-08-28","arxiv_id":"2308.14746","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/covr-learning-composed-video-retrieval-from#ran","syntology_url":"https://syntology.ai/paper/2308.14746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14746"}},"official":{"repos":["lucas-ventura/CoVR"],"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/fire-food-image-to-recipe-generation","slug":"fire-food-image-to-recipe-generation","title":"FIRE: Food Image to REcipe generation","date":"2023-08-28","arxiv_id":"2308.14391","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":2,"n_ran_checked":10,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":16,"phrase":"12 ran (of which 2 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fire-food-image-to-recipe-generation#ran","syntology_url":"https://syntology.ai/paper/2308.14391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14391"}},"official":{"repos":["prateekchhikara/fire"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":2,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/fonmtl-towards-multitask-learning-for-the-fon","slug":"fonmtl-towards-multitask-learning-for-the-fon","title":"FonMTL: Towards Multitask Learning for the Fon Language","date":"2023-08-28","arxiv_id":"2308.14280","repositories_listed":1,"syntology":null},{"url":"/paper/improving-antibody-language-models-with","slug":"improving-antibody-language-models-with","title":"Improving antibody language models with native pairing","date":"2023-08-28","arxiv_id":"2308.14300","repositories_listed":1,"syntology":null},{"url":"/paper/peptidebert-a-language-model-based-on","slug":"peptidebert-a-language-model-based-on","title":"PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction","date":"2023-08-28","arxiv_id":"2309.03099","repositories_listed":1,"syntology":null},{"url":"/paper/textrolspeech-a-text-style-control-speech","slug":"textrolspeech-a-text-style-control-speech","title":"TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech Models","date":"2023-08-28","arxiv_id":"2308.14430","repositories_listed":1,"syntology":null},{"url":"/paper/ores-open-vocabulary-responsible-visual","slug":"ores-open-vocabulary-responsible-visual","title":"ORES: Open-vocabulary Responsible Visual Synthesis","date":"2023-08-26","arxiv_id":"2308.13785","repositories_listed":1,"syntology":null},{"url":"/paper/solving-math-word-problem-with-problem-type","slug":"solving-math-word-problem-with-problem-type","title":"Solving Math Word Problem with Problem Type Classification","date":"2023-08-26","arxiv_id":"2308.13844","repositories_listed":1,"syntology":null},{"url":"/paper/zc3-zero-shot-cross-language-code-clone","slug":"zc3-zero-shot-cross-language-code-clone","title":"ZC3: Zero-Shot Cross-Language Code Clone Detection","date":"2023-08-26","arxiv_id":"2308.13754","repositories_listed":1,"syntology":null},{"url":"/paper/integrating-llms-and-decision-transformers","slug":"integrating-llms-and-decision-transformers","title":"Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity","date":"2023-08-25","arxiv_id":"2308.13278","repositories_listed":1,"syntology":null},{"url":"/paper/prompting-visual-language-models-for-dynamic","slug":"prompting-visual-language-models-for-dynamic","title":"Prompting Visual-Language Models for Dynamic Facial Expression Recognition","date":"2023-08-25","arxiv_id":"2308.13382","repositories_listed":1,"syntology":null},{"url":"/paper/calm-a-multi-task-benchmark-for-comprehensive","slug":"calm-a-multi-task-benchmark-for-comprehensive","title":"CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias","date":"2023-08-24","arxiv_id":"2308.12539","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"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, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/calm-a-multi-task-benchmark-for-comprehensive#ran","syntology_url":"https://syntology.ai/paper/2308.12539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12539"}},"official":{"repos":["vipulgupta1011/calm"],"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/hubo-vlm-unified-vision-language-model","slug":"hubo-vlm-unified-vision-language-model","title":"HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasks","date":"2023-08-24","arxiv_id":"2308.12537","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-language-models-can-perform-many","slug":"diffusion-language-models-can-perform-many","title":"Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning","date":"2023-08-23","arxiv_id":"2308.12219","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":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffusion-language-models-can-perform-many#ran","syntology_url":"https://syntology.ai/paper/2308.12219","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12219"}},"official":{"repos":["yegcjs/diffusionllm"],"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/evolution-of-esg-focused-dlt-research-an-nlp","slug":"evolution-of-esg-focused-dlt-research-an-nlp","title":"Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature","date":"2023-08-23","arxiv_id":"2308.12420","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-protect-copyright-data-in-optimization","slug":"how-to-protect-copyright-data-in-optimization","title":"How to Protect Copyright Data in Optimization of Large Language Models?","date":"2023-08-23","arxiv_id":"2308.12247","repositories_listed":1,"syntology":null},{"url":"/paper/integrating-large-language-models-into-the","slug":"integrating-large-language-models-into-the","title":"Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models","date":"2023-08-23","arxiv_id":"2308.11873","repositories_listed":1,"syntology":null},{"url":"/paper/diversity-measures-domain-independent-proxies","slug":"diversity-measures-domain-independent-proxies","title":"Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries","date":"2023-08-22","arxiv_id":"2308.11189","repositories_listed":1,"syntology":null},{"url":"/paper/multi-event-video-text-retrieval","slug":"multi-event-video-text-retrieval","title":"Multi-event Video-Text Retrieval","date":"2023-08-22","arxiv_id":"2308.11551","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":9,"n_instrument":6,"n_unverified":3,"n_honours":1,"n_violates":1,"n_no_contract":7,"n_pointer_only":18,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/multi-event-video-text-retrieval#ran","syntology_url":"https://syntology.ai/paper/2308.11551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11551"}},"official":{"repos":["gengyuanmax/mevtr"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-training-with-aspect-content-text-mutual","slug":"pre-training-with-aspect-content-text-mutual","title":"Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval","date":"2023-08-22","arxiv_id":"2308.11474","repositories_listed":1,"syntology":null},{"url":"/paper/rella-retrieval-enhanced-large-language","slug":"rella-retrieval-enhanced-large-language","title":"ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation","date":"2023-08-22","arxiv_id":"2308.11131","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":9,"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/rella-retrieval-enhanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2308.11131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11131"}},"official":{"repos":["lavieenrose365/rella"],"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/rosgpt-vision-commanding-robots-using-only","slug":"rosgpt-vision-commanding-robots-using-only","title":"ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts","date":"2023-08-22","arxiv_id":"2308.11236","repositories_listed":1,"syntology":null},{"url":"/paper/artificial-intelligence-is-ineffective-and","slug":"artificial-intelligence-is-ineffective-and","title":"Fact-checking information from large language models can decrease headline discernment","date":"2023-08-21","arxiv_id":"2308.10800","repositories_listed":1,"syntology":null},{"url":"/paper/coca-classifier-oriented-calibration-for","slug":"coca-classifier-oriented-calibration-for","title":"COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation","date":"2023-08-21","arxiv_id":"2308.10450","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-as-a-user-simulator","slug":"large-language-model-as-a-user-simulator","title":"PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator","date":"2023-08-21","arxiv_id":"2308.11534","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":1,"phrase":"0 ran · 2 unverified","sample_list":"/paper/large-language-model-as-a-user-simulator#ran","syntology_url":"https://syntology.ai/paper/2308.11534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.11534"}},"official":null}},{"url":"/paper/seqgpt-an-out-of-the-box-large-language-model","slug":"seqgpt-an-out-of-the-box-large-language-model","title":"SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding","date":"2023-08-21","arxiv_id":"2308.10529","repositories_listed":1,"syntology":null},{"url":"/paper/spikingbert-distilling-bert-to-train-spiking","slug":"spikingbert-distilling-bert-to-train-spiking","title":"SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation","date":"2023-08-21","arxiv_id":"2308.10873","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":8,"n_instrument":5,"n_unverified":4,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":17,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/spikingbert-distilling-bert-to-train-spiking#ran","syntology_url":"https://syntology.ai/paper/2308.10873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10873"}},"official":{"repos":["neurocomplab-psu/spikingbert"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-study-on-robustness-and-reliability-of","slug":"a-study-on-robustness-and-reliability-of","title":"Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation","date":"2023-08-20","arxiv_id":"2308.10335","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":5,"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/a-study-on-robustness-and-reliability-of#ran","syntology_url":"https://syntology.ai/paper/2308.10335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10335"}},"official":{"repos":["floridsleeves/robustapi"],"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/chateda-a-large-language-model-powered","slug":"chateda-a-large-language-model-powered","title":"ChatEDA: A Large Language Model Powered Autonomous Agent for EDA","date":"2023-08-20","arxiv_id":"2308.10204","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/chateda-a-large-language-model-powered#ran","syntology_url":"https://syntology.ai/paper/2308.10204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10204"}},"official":{"repos":["wuhy68/chatedav1"],"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/inductive-bias-learning-generating-code","slug":"inductive-bias-learning-generating-code","title":"Inductive-bias Learning: Generating Code Models with Large Language Model","date":"2023-08-19","arxiv_id":"2308.09890","repositories_listed":1,"syntology":null},{"url":"/paper/biomedgpt-open-multimodal-generative-pre","slug":"biomedgpt-open-multimodal-generative-pre","title":"BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine","date":"2023-08-18","arxiv_id":"2308.09442","repositories_listed":1,"syntology":null},{"url":"/paper/chatharuhi-reviving-anime-character-in","slug":"chatharuhi-reviving-anime-character-in","title":"ChatHaruhi: Reviving Anime Character in Reality via Large Language Model","date":"2023-08-18","arxiv_id":"2308.09597","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/chatharuhi-reviving-anime-character-in#ran","syntology_url":"https://syntology.ai/paper/2308.09597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09597"}},"official":{"repos":["LC1332/Chat-Haruhi-Suzumiya"],"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/latent-state-models-of-training-dynamics","slug":"latent-state-models-of-training-dynamics","title":"Latent State Models of Training Dynamics","date":"2023-08-18","arxiv_id":"2308.09543","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/latent-state-models-of-training-dynamics#ran","syntology_url":"https://syntology.ai/paper/2308.09543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09543"}},"official":{"repos":["michahu/modeling-training"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/language-enhanced-rnr-map-querying-renderable","slug":"language-enhanced-rnr-map-querying-renderable","title":"Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language","date":"2023-08-17","arxiv_id":"2308.08854","repositories_listed":1,"syntology":null},{"url":"/paper/towards-automatically-addressing-self","slug":"towards-automatically-addressing-self","title":"Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?","date":"2023-08-17","arxiv_id":"2308.08943","repositories_listed":1,"syntology":null},{"url":"/paper/bioptimus-pre-training-an-optimal-biomedical","slug":"bioptimus-pre-training-an-optimal-biomedical","title":"BIOptimus: Pre-training an Optimal Biomedical Language Model with Curriculum Learning for Named Entity Recognition","date":"2023-08-16","arxiv_id":"2308.08625","repositories_listed":1,"syntology":null},{"url":"/paper/pevolm-protein-sequence-evolutionary","slug":"pevolm-protein-sequence-evolutionary","title":"PEvoLM: Protein Sequence Evolutionary Information Language Model","date":"2023-08-16","arxiv_id":"2308.08578","repositories_listed":1,"syntology":null},{"url":"/paper/separate-the-wheat-from-the-chaff-model","slug":"separate-the-wheat-from-the-chaff-model","title":"Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation","date":"2023-08-16","arxiv_id":"2308.08090","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/separate-the-wheat-from-the-chaff-model#ran","syntology_url":"https://syntology.ai/paper/2308.08090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08090"}},"official":{"repos":["hitsz-tmg/ext-sub"],"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/test-text-prototype-aligned-embedding-to","slug":"test-text-prototype-aligned-embedding-to","title":"TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series","date":"2023-08-16","arxiv_id":"2308.08241","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/test-text-prototype-aligned-embedding-to#ran","syntology_url":"https://syntology.ai/paper/2308.08241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.08241"}},"official":{"repos":["scxsunchenxi/test"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/visually-aware-context-modeling-for-news","slug":"visually-aware-context-modeling-for-news","title":"Visually-Aware Context Modeling for News Image Captioning","date":"2023-08-16","arxiv_id":"2308.08325","repositories_listed":1,"syntology":null},{"url":"/paper/a-foundation-language-image-model-of-the","slug":"a-foundation-language-image-model-of-the","title":"A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision","date":"2023-08-15","arxiv_id":"2308.07898","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/a-foundation-language-image-model-of-the#ran","syntology_url":"https://syntology.ai/paper/2308.07898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07898"}},"official":{"repos":["jusiro/flair"],"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/raven-in-context-learning-with-retrieval","slug":"raven-in-context-learning-with-retrieval","title":"RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models","date":"2023-08-15","arxiv_id":"2308.07922","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/raven-in-context-learning-with-retrieval#ran","syntology_url":"https://syntology.ai/paper/2308.07922","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07922"}},"official":{"repos":["jeffhj/raven"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ternary-singular-value-decomposition-as-a","slug":"ternary-singular-value-decomposition-as-a","title":"Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping","date":"2023-08-15","arxiv_id":"2308.07641","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ternary-singular-value-decomposition-as-a#ran","syntology_url":"https://syntology.ai/paper/2308.07641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07641"}},"official":{"repos":["ozzzp/ternary_decompose"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-flow-networks","slug":"bayesian-flow-networks","title":"Bayesian Flow Networks","date":"2023-08-14","arxiv_id":"2308.07037","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":9,"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) · 1 unverified","sample_list":"/paper/bayesian-flow-networks#ran","syntology_url":"https://syntology.ai/paper/2308.07037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07037"}},"official":{"repos":["nnaisense/bayesian-flow-networks"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/causallm-is-not-optimal-for-in-context","slug":"causallm-is-not-optimal-for-in-context","title":"CausalLM is not optimal for in-context learning","date":"2023-08-14","arxiv_id":"2308.06912","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/causallm-is-not-optimal-for-in-context#ran","syntology_url":"https://syntology.ai/paper/2308.06912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06912"}},"official":{"repos":["google-research/causallm_icl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/ecomgpt-instruction-tuning-large-language","slug":"ecomgpt-instruction-tuning-large-language","title":"EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce","date":"2023-08-14","arxiv_id":"2308.06966","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/ecomgpt-instruction-tuning-large-language#ran","syntology_url":"https://syntology.ai/paper/2308.06966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06966"}},"official":{"repos":["Alibaba-NLP/EcomGPT"],"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/git-mol-a-multi-modal-large-language-model","slug":"git-mol-a-multi-modal-large-language-model","title":"GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text","date":"2023-08-14","arxiv_id":"2308.06911","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/git-mol-a-multi-modal-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2308.06911","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06911"}},"official":{"repos":["ai-hpc-research-team/git-mol"],"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/llm-self-defense-by-self-examination-llms","slug":"llm-self-defense-by-self-examination-llms","title":"LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked","date":"2023-08-14","arxiv_id":"2308.07308","repositories_listed":1,"syntology":null},{"url":"/paper/natural-language-is-all-a-graph-needs","slug":"natural-language-is-all-a-graph-needs","title":"Language is All a Graph Needs","date":"2023-08-14","arxiv_id":"2308.07134","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/natural-language-is-all-a-graph-needs#ran","syntology_url":"https://syntology.ai/paper/2308.07134","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07134"}},"official":{"repos":["agiresearch/InstructGLM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-authorship-attribution-stylometric","slug":"neural-authorship-attribution-stylometric","title":"Neural Authorship Attribution: Stylometric Analysis on Large Language Models","date":"2023-08-14","arxiv_id":"2308.07305","repositories_listed":1,"syntology":null},{"url":"/paper/pairing-interacting-protein-sequences-using","slug":"pairing-interacting-protein-sequences-using","title":"Pairing interacting protein sequences using masked language modeling","date":"2023-08-14","arxiv_id":"2308.07136","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/pairing-interacting-protein-sequences-using#ran","syntology_url":"https://syntology.ai/paper/2308.07136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.07136"}},"official":{"repos":["bitbol-lab/diffpalm"],"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/littlemu-deploying-an-online-virtual-teaching","slug":"littlemu-deploying-an-online-virtual-teaching","title":"LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts","date":"2023-08-11","arxiv_id":"2308.05935","repositories_listed":1,"syntology":null},{"url":"/paper/rtllm-an-open-source-benchmark-for-design-rtl","slug":"rtllm-an-open-source-benchmark-for-design-rtl","title":"RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model","date":"2023-08-10","arxiv_id":"2308.05345","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rtllm-an-open-source-benchmark-for-design-rtl#ran","syntology_url":"https://syntology.ai/paper/2308.05345","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05345"}},"official":{"repos":["hkust-zhiyao/rtllm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/weaverbird-empowering-financial-decision","slug":"weaverbird-empowering-financial-decision","title":"WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine","date":"2023-08-10","arxiv_id":"2308.05361","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-multilingual-text-data-distillation","slug":"exploring-multilingual-text-data-distillation","title":"Exploring Multilingual Text Data Distillation","date":"2023-08-09","arxiv_id":"2308.04982","repositories_listed":1,"syntology":null},{"url":"/paper/prompting-in-context-operator-learning-with","slug":"prompting-in-context-operator-learning-with","title":"Fine-Tune Language Models as Multi-Modal Differential Equation Solvers","date":"2023-08-09","arxiv_id":"2308.05061","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":1,"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/prompting-in-context-operator-learning-with#ran","syntology_url":"https://syntology.ai/paper/2308.05061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.05061"}},"official":{"repos":["liuyangmage/in-context-operator-networks"],"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/slot-induction-via-pre-trained-language-model","slug":"slot-induction-via-pre-trained-language-model","title":"Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning","date":"2023-08-09","arxiv_id":"2308.04712","repositories_listed":1,"syntology":null},{"url":"/paper/agentsims-an-open-source-sandbox-for-large","slug":"agentsims-an-open-source-sandbox-for-large","title":"AgentSims: An Open-Source Sandbox for Large Language Model Evaluation","date":"2023-08-08","arxiv_id":"2308.04026","repositories_listed":1,"syntology":null},{"url":"/paper/in-context-alignment-chat-with-vanilla","slug":"in-context-alignment-chat-with-vanilla","title":"In-Context Alignment: Chat with Vanilla Language Models Before Fine-Tuning","date":"2023-08-08","arxiv_id":"2308.04275","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/in-context-alignment-chat-with-vanilla#ran","syntology_url":"https://syntology.ai/paper/2308.04275","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.04275"}},"official":{"repos":["xhan77/in-context-alignment"],"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","unlocated"]}}},{"url":"/paper/on-monotonic-aggregation-for-open-domain-qa","slug":"on-monotonic-aggregation-for-open-domain-qa","title":"On Monotonic Aggregation for Open-domain QA","date":"2023-08-08","arxiv_id":"2308.04176","repositories_listed":1,"syntology":null},{"url":"/paper/ptransips-identification-of-phosphorylation","slug":"ptransips-identification-of-phosphorylation","title":"PTransIPs: Identification of phosphorylation sites enhanced by protein PLM embeddings","date":"2023-08-08","arxiv_id":"2308.05115","repositories_listed":1,"syntology":null},{"url":"/paper/shepherd-a-critic-for-language-model","slug":"shepherd-a-critic-for-language-model","title":"Shepherd: A Critic for Language Model Generation","date":"2023-08-08","arxiv_id":"2308.04592","repositories_listed":1,"syntology":null},{"url":"/paper/silo-language-models-isolating-legal-risk-in","slug":"silo-language-models-isolating-legal-risk-in","title":"SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore","date":"2023-08-08","arxiv_id":"2308.04430","repositories_listed":1,"syntology":null},{"url":"/paper/simplyretrieve-a-private-and-lightweight","slug":"simplyretrieve-a-private-and-lightweight","title":"SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool","date":"2023-08-08","arxiv_id":"2308.03983","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-detailed-oncologic-history-and","slug":"extracting-detailed-oncologic-history-and","title":"CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference","date":"2023-08-07","arxiv_id":"2308.03853","repositories_listed":1,"syntology":null},{"url":"/paper/kitlm-domain-specific-knowledge-integration","slug":"kitlm-domain-specific-knowledge-integration","title":"KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering","date":"2023-08-07","arxiv_id":"2308.03638","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-preserving-pruning-for-pre-trained","slug":"knowledge-preserving-pruning-for-pre-trained","title":"Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models","date":"2023-08-07","arxiv_id":"2308.03449","repositories_listed":1,"syntology":{"n":16,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":16,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/knowledge-preserving-pruning-for-pre-trained#ran","syntology_url":"https://syntology.ai/paper/2308.03449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03449"}},"official":{"repos":["snudm-starlab/k-prune"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/medmine-examining-pre-trained-language-models","slug":"medmine-examining-pre-trained-language-models","title":"MedMine: Examining Pre-trained Language Models on Medication Mining","date":"2023-08-07","arxiv_id":"2308.03629","repositories_listed":1,"syntology":null},{"url":"/paper/rcmha-relative-convolutional-multi-head","slug":"rcmha-relative-convolutional-multi-head","title":"RCMHA: Relative Convolutional Multi-Head Attention for Natural Language Modelling","date":"2023-08-07","arxiv_id":"2308.03429","repositories_listed":1,"syntology":null},{"url":"/paper/vilp-knowledge-exploration-using-vision","slug":"vilp-knowledge-exploration-using-vision","title":"ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition","date":"2023-08-07","arxiv_id":"2308.03908","repositories_listed":1,"syntology":null},{"url":"/paper/zhongjing-enhancing-the-chinese-medical","slug":"zhongjing-enhancing-the-chinese-medical","title":"Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue","date":"2023-08-07","arxiv_id":"2308.03549","repositories_listed":1,"syntology":null},{"url":"/paper/larch-large-language-model-based-automatic","slug":"larch-large-language-model-based-automatic","title":"LARCH: Large Language Model-based Automatic Readme Creation with Heuristics","date":"2023-08-06","arxiv_id":"2308.03099","repositories_listed":1,"syntology":null},{"url":"/paper/reclip-refine-contrastive-language-image-pre","slug":"reclip-refine-contrastive-language-image-pre","title":"ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation","date":"2023-08-04","arxiv_id":"2308.03793","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/reclip-refine-contrastive-language-image-pre#ran","syntology_url":"https://syntology.ai/paper/2308.03793","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03793"}},"official":{"repos":["michiganleon/reclip_wacv"],"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/retroformer-retrospective-large-language","slug":"retroformer-retrospective-large-language","title":"Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization","date":"2023-08-04","arxiv_id":"2308.02151","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/retroformer-retrospective-large-language#ran","syntology_url":"https://syntology.ai/paper/2308.02151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.02151"}},"official":{"repos":["weirayao/retroformer"],"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/conceptlab-creative-generation-using","slug":"conceptlab-creative-generation-using","title":"ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints","date":"2023-08-03","arxiv_id":"2308.02669","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/conceptlab-creative-generation-using#ran","syntology_url":"https://syntology.ai/paper/2308.02669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.02669"}},"official":{"repos":["kfirgoldberg/ConceptLab"],"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/specious-sites-tracking-the-spread-and-sway","slug":"specious-sites-tracking-the-spread-and-sway","title":"Specious Sites: Tracking the Spread and Sway of Spurious News Stories at Scale","date":"2023-08-03","arxiv_id":"2308.02068","repositories_listed":1,"syntology":null},{"url":"/paper/a-practical-deep-learning-based-acoustic-side","slug":"a-practical-deep-learning-based-acoustic-side","title":"A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards","date":"2023-08-02","arxiv_id":"2308.01074","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-emotion-recognition-using","slug":"contextual-emotion-recognition-using","title":"Contextual Emotion Recognition Using Transformer-Based Models","date":"2023-08-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/do-multilingual-language-models-think-better","slug":"do-multilingual-language-models-think-better","title":"Do Multilingual Language Models Think Better in English?","date":"2023-08-02","arxiv_id":"2308.01223","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/do-multilingual-language-models-think-better#ran","syntology_url":"https://syntology.ai/paper/2308.01223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01223"}},"official":{"repos":["juletx/self-translate"],"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"]}}}],"record_sha256":"db8b2637a688aa991e8c1d01725d823abc17a3ea37f553a3fddad2ae0d97f69b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}