{"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/40","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":40,"pages_in_order":142,"rows_per_page":100,"rows":[3901,4000],"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/39","next":"/task/language-modeling/papers/41","papers":[{"url":"/paper/prompt-learning-to-mitigate-catastrophic","slug":"prompt-learning-to-mitigate-catastrophic","title":"Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation","date":"2023-05-12","arxiv_id":"2305.07393","repositories_listed":1,"syntology":null},{"url":"/paper/musketeer-all-for-one-and-one-for-all-a","slug":"musketeer-all-for-one-and-one-for-all-a","title":"Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts","date":"2023-05-11","arxiv_id":"2305.07019","repositories_listed":1,"syntology":null},{"url":"/paper/self-chained-image-language-model-for-video-1","slug":"self-chained-image-language-model-for-video-1","title":"Self-Chained Image-Language Model for Video Localization and Question Answering","date":"2023-05-11","arxiv_id":"2305.06988","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/self-chained-image-language-model-for-video-1#ran","syntology_url":"https://syntology.ai/paper/2305.06988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06988"}},"official":{"repos":["yui010206/sevila"],"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/automatic-evaluation-of-attribution-by-large","slug":"automatic-evaluation-of-attribution-by-large","title":"Automatic Evaluation of Attribution by Large Language Models","date":"2023-05-10","arxiv_id":"2305.06311","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/automatic-evaluation-of-attribution-by-large#ran","syntology_url":"https://syntology.ai/paper/2305.06311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06311"}},"official":{"repos":["osu-nlp-group/attrscore"],"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/bot-or-human-detecting-chatgpt-imposters-with","slug":"bot-or-human-detecting-chatgpt-imposters-with","title":"Bot or Human? Detecting ChatGPT Imposters with A Single Question","date":"2023-05-10","arxiv_id":"2305.06424","repositories_listed":1,"syntology":null},{"url":"/paper/enriching-language-models-with-graph-based","slug":"enriching-language-models-with-graph-based","title":"Enriching language models with graph-based context information to better understand textual data","date":"2023-05-10","arxiv_id":"2305.11070","repositories_listed":1,"syntology":null},{"url":"/paper/say-what-you-mean-large-language-models-speak","slug":"say-what-you-mean-large-language-models-speak","title":"Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge","date":"2023-05-10","arxiv_id":"2305.05976","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/say-what-you-mean-large-language-models-speak#ran","syntology_url":"https://syntology.ai/paper/2305.05976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05976"}},"official":{"repos":["jiangjiechen/uncommongen"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/detection-of-depression-on-social-networks","slug":"detection-of-depression-on-social-networks","title":"Detection of depression on social networks using transformers and ensembles","date":"2023-05-09","arxiv_id":"2305.05325","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-modal-context-reasoning-approach-for","slug":"a-multi-modal-context-reasoning-approach-for","title":"A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues","date":"2023-05-08","arxiv_id":"2305.04530","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-gpt-a-vision-and-language-model","slug":"multimodal-gpt-a-vision-and-language-model","title":"MultiModal-GPT: A Vision and Language Model for Dialogue with Humans","date":"2023-05-08","arxiv_id":"2305.04790","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/multimodal-gpt-a-vision-and-language-model#ran","syntology_url":"https://syntology.ai/paper/2305.04790","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04790"}},"official":{"repos":["open-mmlab/multimodal-gpt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/refining-the-responses-of-llms-by-themselves","slug":"refining-the-responses-of-llms-by-themselves","title":"Refining the Responses of LLMs by Themselves","date":"2023-05-06","arxiv_id":"2305.04039","repositories_listed":1,"syntology":null},{"url":"/paper/a-low-resource-approach-to-the-grammatical","slug":"a-low-resource-approach-to-the-grammatical","title":"A Low-Resource Approach to the Grammatical Error Correction of Ukrainian","date":"2023-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/t-sciq-teaching-multimodal-chain-of-thought","slug":"t-sciq-teaching-multimodal-chain-of-thought","title":"T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering","date":"2023-05-05","arxiv_id":"2305.03453","repositories_listed":1,"syntology":null},{"url":"/paper/2x-faster-language-model-pre-training-via","slug":"2x-faster-language-model-pre-training-via","title":"Masked Structural Growth for 2x Faster Language Model Pre-training","date":"2023-05-04","arxiv_id":"2305.02869","repositories_listed":1,"syntology":{"n":34,"n_ran":16,"n_constructed":6,"n_ran_checked":10,"n_instrument":6,"n_unverified":18,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"16 ran (of which 6 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 6 where Syntology's instrument failed) · 18 unverified","sample_list":"/paper/2x-faster-language-model-pre-training-via#ran","syntology_url":"https://syntology.ai/paper/2305.02869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02869"}},"official":{"repos":["cofe-ai/msg"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":6,"n_ran_no_instrument_failure":10,"n_unverified":18,"ran_from_kinds":["official"]}}},{"url":"/paper/chatgpt-steered-editing-instructor-for","slug":"chatgpt-steered-editing-instructor-for","title":"Personalized Abstractive Summarization by Tri-agent Generation Pipeline","date":"2023-05-04","arxiv_id":"2305.02483","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-expressivity-role-of-layernorm-in","slug":"on-the-expressivity-role-of-layernorm-in","title":"On the Expressivity Role of LayerNorm in Transformers' Attention","date":"2023-05-04","arxiv_id":"2305.02582","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":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/on-the-expressivity-role-of-layernorm-in#ran","syntology_url":"https://syntology.ai/paper/2305.02582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02582"}},"official":{"repos":["tech-srl/layer_norm_expressivity_role"],"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/sentence-embedding-leaks-more-information","slug":"sentence-embedding-leaks-more-information","title":"Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence","date":"2023-05-04","arxiv_id":"2305.03010","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/sentence-embedding-leaks-more-information#ran","syntology_url":"https://syntology.ai/paper/2305.03010","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03010"}},"official":{"repos":["hkust-knowcomp/geia"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/chatgraph-interpretable-text-classification","slug":"chatgraph-interpretable-text-classification","title":"ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs","date":"2023-05-03","arxiv_id":"2305.03513","repositories_listed":1,"syntology":null},{"url":"/paper/defending-against-insertion-based-textual","slug":"defending-against-insertion-based-textual","title":"Defending against Insertion-based Textual Backdoor Attacks via Attribution","date":"2023-05-03","arxiv_id":"2305.02394","repositories_listed":1,"syntology":null},{"url":"/paper/entity-tracking-in-language-models","slug":"entity-tracking-in-language-models","title":"Entity Tracking in Language Models","date":"2023-05-03","arxiv_id":"2305.02363","repositories_listed":1,"syntology":null},{"url":"/paper/huatuo-26m-a-large-scale-chinese-medical-qa","slug":"huatuo-26m-a-large-scale-chinese-medical-qa","title":"Huatuo-26M, a Large-scale Chinese Medical QA Dataset","date":"2023-05-02","arxiv_id":"2305.01526","repositories_listed":1,"syntology":null},{"url":"/paper/the-benefits-of-bad-advice-autocontrastive","slug":"the-benefits-of-bad-advice-autocontrastive","title":"The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers","date":"2023-05-02","arxiv_id":"2305.01628","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":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) · 0 unverified","sample_list":"/paper/the-benefits-of-bad-advice-autocontrastive#ran","syntology_url":"https://syntology.ai/paper/2305.01628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.01628"}},"official":{"repos":["ibm/auto-contrastive-generation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tallrec-an-effective-and-efficient-tuning","slug":"tallrec-an-effective-and-efficient-tuning","title":"TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation","date":"2023-04-30","arxiv_id":"2305.00447","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tallrec-an-effective-and-efficient-tuning#ran","syntology_url":"https://syntology.ai/paper/2305.00447","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00447"}},"official":{"repos":["sai990323/tallrec"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ccpdf-building-a-high-quality-corpus-for","slug":"ccpdf-building-a-high-quality-corpus-for","title":"CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data","date":"2023-04-28","arxiv_id":"2304.14953","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-verbal-reasoner-plus-evr-a","slug":"explainable-verbal-reasoner-plus-evr-a","title":"Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning","date":"2023-04-28","arxiv_id":"2305.00061","repositories_listed":1,"syntology":null},{"url":"/paper/outline-then-details-syntactically-guided","slug":"outline-then-details-syntactically-guided","title":"Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation","date":"2023-04-28","arxiv_id":"2305.00909","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":1,"n_ran_checked":5,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/outline-then-details-syntactically-guided#ran","syntology_url":"https://syntology.ai/paper/2305.00909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00909"}},"official":{"repos":["vita-group/chaincoder"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-autonomous-system-flexible-modular","slug":"towards-autonomous-system-flexible-modular","title":"Towards autonomous system: flexible modular production system enhanced with large language model agents","date":"2023-04-28","arxiv_id":"2304.14721","repositories_listed":1,"syntology":null},{"url":"/paper/a-modular-approach-for-multilingual-timex","slug":"a-modular-approach-for-multilingual-timex","title":"A Modular Approach for Multilingual Timex Detection and Normalization using Deep Learning and Grammar-based methods","date":"2023-04-27","arxiv_id":"2304.14221","repositories_listed":1,"syntology":null},{"url":"/paper/pmc-llama-further-finetuning-llama-on-medical","slug":"pmc-llama-further-finetuning-llama-on-medical","title":"PMC-LLaMA: Towards Building Open-source Language Models for Medicine","date":"2023-04-27","arxiv_id":"2304.14454","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/pmc-llama-further-finetuning-llama-on-medical#ran","syntology_url":"https://syntology.ai/paper/2304.14454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.14454"}},"official":{"repos":["chaoyi-wu/pmc-llama"],"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/swectrl-mini-a-data-transparent-transformer","slug":"swectrl-mini-a-data-transparent-transformer","title":"SweCTRL-Mini: a data-transparent Transformer-based large language model for controllable text generation in Swedish","date":"2023-04-27","arxiv_id":"2304.13994","repositories_listed":1,"syntology":null},{"url":"/paper/uio-at-semeval-2023-task-12-multilingual-fine","slug":"uio-at-semeval-2023-task-12-multilingual-fine","title":"UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource languages","date":"2023-04-27","arxiv_id":"2304.14189","repositories_listed":1,"syntology":null},{"url":"/paper/vision-conformer-incorporating-convolutions","slug":"vision-conformer-incorporating-convolutions","title":"Vision Conformer: Incorporating Convolutions into Vision Transformer Layers","date":"2023-04-27","arxiv_id":"2304.13991","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-infinite-length-input-capacity-for","slug":"unleashing-infinite-length-input-capacity-for","title":"Enhancing Large Language Model with Self-Controlled Memory Framework","date":"2023-04-26","arxiv_id":"2304.13343","repositories_listed":1,"syntology":{"n":14,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":11,"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) · 11 unverified","sample_list":"/paper/unleashing-infinite-length-input-capacity-for#ran","syntology_url":"https://syntology.ai/paper/2304.13343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13343"}},"official":{"repos":["wbbeyourself/scm4llms"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/a-lightweight-constrained-generation","slug":"a-lightweight-constrained-generation","title":"A Lightweight Constrained Generation Alternative for Query-focused Summarization","date":"2023-04-23","arxiv_id":"2304.11721","repositories_listed":1,"syntology":null},{"url":"/paper/sailer-structure-aware-pre-trained-language","slug":"sailer-structure-aware-pre-trained-language","title":"SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval","date":"2023-04-22","arxiv_id":"2304.11370","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/sailer-structure-aware-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2304.11370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11370"}},"official":{"repos":["cshaitao/sailer"],"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/evaluating-transformer-language-models-on-1","slug":"evaluating-transformer-language-models-on-1","title":"Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition","date":"2023-04-21","arxiv_id":"2304.10977","repositories_listed":1,"syntology":null},{"url":"/paper/kitchenscale-learning-to-predict-ingredient","slug":"kitchenscale-learning-to-predict-ingredient","title":"KitchenScale: Learning to predict ingredient quantities from recipe contexts","date":"2023-04-21","arxiv_id":"2304.10739","repositories_listed":1,"syntology":null},{"url":"/paper/phoenix-democratizing-chatgpt-across","slug":"phoenix-democratizing-chatgpt-across","title":"Phoenix: Democratizing ChatGPT across Languages","date":"2023-04-20","arxiv_id":"2304.10453","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/phoenix-democratizing-chatgpt-across#ran","syntology_url":"https://syntology.ai/paper/2304.10453","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.10453"}},"official":{"repos":["freedomintelligence/llmzoo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/brent-bidirectional-retrieval-enhanced","slug":"brent-bidirectional-retrieval-enhanced","title":"BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer","date":"2023-04-19","arxiv_id":"2304.09649","repositories_listed":1,"syntology":null},{"url":"/paper/cb-conformer-contextual-biasing-conformer-for","slug":"cb-conformer-contextual-biasing-conformer-for","title":"CB-Conformer: Contextual biasing Conformer for biased word recognition","date":"2023-04-19","arxiv_id":"2304.09607","repositories_listed":1,"syntology":null},{"url":"/paper/masked-language-model-based-textual","slug":"masked-language-model-based-textual","title":"Masked Language Model Based Textual Adversarial Example Detection","date":"2023-04-18","arxiv_id":"2304.08767","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-between-full-parameter","slug":"a-comparative-study-between-full-parameter","title":"A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model","date":"2023-04-17","arxiv_id":"2304.08109","repositories_listed":1,"syntology":null},{"url":"/paper/skillgpt-a-restful-api-service-for-skill","slug":"skillgpt-a-restful-api-service-for-skill","title":"SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model","date":"2023-04-17","arxiv_id":"2304.11060","repositories_listed":1,"syntology":null},{"url":"/paper/the-minipile-challenge-for-data-efficient","slug":"the-minipile-challenge-for-data-efficient","title":"The MiniPile Challenge for Data-Efficient Language Models","date":"2023-04-17","arxiv_id":"2304.08442","repositories_listed":1,"syntology":null},{"url":"/paper/neural-machine-translation-for-low-resource-3","slug":"neural-machine-translation-for-low-resource-3","title":"Neural Machine Translation For Low Resource Languages","date":"2023-04-16","arxiv_id":"2304.07869","repositories_listed":1,"syntology":null},{"url":"/paper/solving-math-word-problems-by-combining","slug":"solving-math-word-problems-by-combining","title":"Solving Math Word Problems by Combining Language Models With Symbolic Solvers","date":"2023-04-16","arxiv_id":"2304.09102","repositories_listed":1,"syntology":null},{"url":"/paper/tagclip-improving-discrimination-ability-of","slug":"tagclip-improving-discrimination-ability-of","title":"TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation","date":"2023-04-15","arxiv_id":"2304.07547","repositories_listed":1,"syntology":null},{"url":"/paper/openassistant-conversations-democratizing","slug":"openassistant-conversations-democratizing","title":"OpenAssistant Conversations -- Democratizing Large Language Model Alignment","date":"2023-04-14","arxiv_id":"2304.07327","repositories_listed":1,"syntology":null},{"url":"/paper/lasuie-unifying-information-extraction-with","slug":"lasuie-unifying-information-extraction-with","title":"LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model","date":"2023-04-13","arxiv_id":"2304.06248","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lasuie-unifying-information-extraction-with#ran","syntology_url":"https://syntology.ai/paper/2304.06248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06248"}},"official":{"repos":["chocowu/lasuie"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/priors-for-symbolic-regression","slug":"priors-for-symbolic-regression","title":"Priors for symbolic regression","date":"2023-04-13","arxiv_id":"2304.06333","repositories_listed":1,"syntology":null},{"url":"/paper/boosted-prompt-ensembles-for-large-language","slug":"boosted-prompt-ensembles-for-large-language","title":"Boosted Prompt Ensembles for Large Language Models","date":"2023-04-12","arxiv_id":"2304.05970","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-learning-for-news-recommendation","slug":"prompt-learning-for-news-recommendation","title":"Prompt Learning for News Recommendation","date":"2023-04-11","arxiv_id":"2304.05263","repositories_listed":1,"syntology":null},{"url":"/paper/r-softmax-generalized-softmax-with","slug":"r-softmax-generalized-softmax-with","title":"r-softmax: Generalized Softmax with Controllable Sparsity Rate","date":"2023-04-11","arxiv_id":"2304.05243","repositories_listed":1,"syntology":null},{"url":"/paper/a-cheaper-and-better-diffusion-language-model","slug":"a-cheaper-and-better-diffusion-language-model","title":"A Cheaper and Better Diffusion Language Model with Soft-Masked Noise","date":"2023-04-10","arxiv_id":"2304.04746","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-cheaper-and-better-diffusion-language-model#ran","syntology_url":"https://syntology.ai/paper/2304.04746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04746"}},"official":{"repos":["amazon-science/masked-diffusion-lm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/interaction-aware-prompting-for-zero-shot","slug":"interaction-aware-prompting-for-zero-shot","title":"Interaction-Aware Prompting for Zero-Shot Spatio-Temporal Action Detection","date":"2023-04-10","arxiv_id":"2304.04688","repositories_listed":1,"syntology":null},{"url":"/paper/selformer-molecular-representation-learning","slug":"selformer-molecular-representation-learning","title":"SELFormer: Molecular Representation Learning via SELFIES Language Models","date":"2023-04-10","arxiv_id":"2304.04662","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-natural-language-inference","slug":"uncertainty-aware-natural-language-inference","title":"Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging","date":"2023-04-10","arxiv_id":"2304.04726","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/uncertainty-aware-natural-language-inference#ran","syntology_url":"https://syntology.ai/paper/2304.04726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04726"}},"official":{"repos":["helsinki-nlp/uncertainty-aware-nli"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/making-ai-less-thirsty-uncovering-and","slug":"making-ai-less-thirsty-uncovering-and","title":"Making AI Less \"Thirsty\": Uncovering and Addressing the Secret Water Footprint of AI Models","date":"2023-04-06","arxiv_id":"2304.03271","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/making-ai-less-thirsty-uncovering-and#ran","syntology_url":"https://syntology.ai/paper/2304.03271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03271"}},"official":{"repos":["ren-research/making-ai-less-thirsty"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-ocr-for-building-a-diverse-digital","slug":"efficient-ocr-for-building-a-diverse-digital","title":"Efficient OCR for Building a Diverse Digital History","date":"2023-04-05","arxiv_id":"2304.02737","repositories_listed":1,"syntology":null},{"url":"/paper/synthesize-extremely-high-dimensional","slug":"synthesize-extremely-high-dimensional","title":"Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model","date":"2023-04-04","arxiv_id":"2304.02169","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/synthesize-extremely-high-dimensional#ran","syntology_url":"https://syntology.ai/paper/2304.02169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.02169"}},"official":{"repos":["btheodorou99/halo_inpatient"],"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/unsupervised-improvement-of-factual-knowledge","slug":"unsupervised-improvement-of-factual-knowledge","title":"Unsupervised Improvement of Factual Knowledge in Language Models","date":"2023-04-04","arxiv_id":"2304.01597","repositories_listed":1,"syntology":null},{"url":"/paper/peach-pre-training-sequence-to-sequence","slug":"peach-pre-training-sequence-to-sequence","title":"PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation","date":"2023-04-03","arxiv_id":"2304.01282","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-semantic-segmentation-with","slug":"zero-shot-semantic-segmentation-with","title":"Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network","date":"2023-04-03","arxiv_id":"2304.01198","repositories_listed":1,"syntology":null},{"url":"/paper/dera-enhancing-large-language-model","slug":"dera-enhancing-large-language-model","title":"DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents","date":"2023-03-30","arxiv_id":"2303.17071","repositories_listed":1,"syntology":null},{"url":"/paper/prefix-tuning-for-automated-audio-captioning","slug":"prefix-tuning-for-automated-audio-captioning","title":"Prefix tuning for automated audio captioning","date":"2023-03-30","arxiv_id":"2303.17489","repositories_listed":1,"syntology":null},{"url":"/paper/hallucinations-in-large-multilingual","slug":"hallucinations-in-large-multilingual","title":"Hallucinations in Large Multilingual Translation Models","date":"2023-03-28","arxiv_id":"2303.16104","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/hallucinations-in-large-multilingual#ran","syntology_url":"https://syntology.ai/paper/2303.16104","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16104"}},"official":{"repos":["deep-spin/lmt_hallucinations"],"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/training-language-models-with-language","slug":"training-language-models-with-language","title":"Training Language Models with Language Feedback at Scale","date":"2023-03-28","arxiv_id":"2303.16755","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/training-language-models-with-language#ran","syntology_url":"https://syntology.ai/paper/2303.16755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16755"}},"official":{"repos":["jeremyalain/imitation_learning_from_language_feedback"],"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/fine-grained-audible-video-description","slug":"fine-grained-audible-video-description","title":"Fine-grained Audible Video Description","date":"2023-03-27","arxiv_id":"2303.15616","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/fine-grained-audible-video-description#ran","syntology_url":"https://syntology.ai/paper/2303.15616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.15616"}},"official":{"repos":["opennlplab/favdbench"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/ifseg-image-free-semantic-segmentation-via","slug":"ifseg-image-free-semantic-segmentation-via","title":"IFSeg: Image-free Semantic Segmentation via Vision-Language Model","date":"2023-03-25","arxiv_id":"2303.14396","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-vision-language-pretraining-with","slug":"accelerating-vision-language-pretraining-with","title":"Accelerating Vision-Language Pretraining with Free Language Modeling","date":"2023-03-24","arxiv_id":"2303.14038","repositories_listed":1,"syntology":null},{"url":"/paper/chatdoctor-a-medical-chat-model-fine-tuned-on","slug":"chatdoctor-a-medical-chat-model-fine-tuned-on","title":"ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge","date":"2023-03-24","arxiv_id":"2303.14070","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-tuning-based-adapter-for-vision","slug":"prompt-tuning-based-adapter-for-vision","title":"Prompt Tuning based Adapter for Vision-Language Model Adaption","date":"2023-03-24","arxiv_id":"2303.15234","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-expert-language-models-with","slug":"scaling-expert-language-models-with","title":"Scaling Expert Language Models with Unsupervised Domain Discovery","date":"2023-03-24","arxiv_id":"2303.14177","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 3 unverified","sample_list":"/paper/scaling-expert-language-models-with#ran","syntology_url":"https://syntology.ai/paper/2303.14177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.14177"}},"official":{"repos":["kernelmachine/cbtm"],"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","unlocated"]}}},{"url":"/paper/video-pre-trained-transformer-a-multimodal","slug":"video-pre-trained-transformer-a-multimodal","title":"Video Pre-trained Transformer: A Multimodal Mixture of Pre-trained Experts","date":"2023-03-24","arxiv_id":"2304.10505","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-structured-semantic-prior-for-multi","slug":"exploring-structured-semantic-prior-for-multi","title":"Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels","date":"2023-03-23","arxiv_id":"2303.13223","repositories_listed":1,"syntology":null},{"url":"/paper/gett-qa-graph-embedding-based-t2t-transformer","slug":"gett-qa-graph-embedding-based-t2t-transformer","title":"GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering","date":"2023-03-23","arxiv_id":"2303.13284","repositories_listed":1,"syntology":null},{"url":"/paper/modular-retrieval-for-generalization-and","slug":"modular-retrieval-for-generalization-and","title":"Modular Retrieval for Generalization and Interpretation","date":"2023-03-23","arxiv_id":"2303.13419","repositories_listed":1,"syntology":null},{"url":"/paper/swissbert-the-multilingual-language-model-for","slug":"swissbert-the-multilingual-language-model-for","title":"SwissBERT: The Multilingual Language Model for Switzerland","date":"2023-03-23","arxiv_id":"2303.13310","repositories_listed":1,"syntology":null},{"url":"/paper/the-quantization-model-of-neural-scaling","slug":"the-quantization-model-of-neural-scaling","title":"The Quantization Model of Neural Scaling","date":"2023-03-23","arxiv_id":"2303.13506","repositories_listed":1,"syntology":null},{"url":"/paper/visual-language-prompt-tuning-with-knowledge","slug":"visual-language-prompt-tuning-with-knowledge","title":"Visual-Language Prompt Tuning with Knowledge-guided Context Optimization","date":"2023-03-23","arxiv_id":"2303.13283","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":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/visual-language-prompt-tuning-with-knowledge#ran","syntology_url":"https://syntology.ai/paper/2303.13283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13283"}},"official":{"repos":["htyao89/kgcoop"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/visually-prompted-language-model-for-fine","slug":"visually-prompted-language-model-for-fine","title":"Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World","date":"2023-03-23","arxiv_id":"2303.13233","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/visually-prompted-language-model-for-fine#ran","syntology_url":"https://syntology.ai/paper/2303.13233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13233"}},"official":{"repos":["Yuqifan1117/CaCao"],"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/cross-modal-implicit-relation-reasoning-and","slug":"cross-modal-implicit-relation-reasoning-and","title":"Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval","date":"2023-03-22","arxiv_id":"2303.12501","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":3,"n_ran_checked":4,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/cross-modal-implicit-relation-reasoning-and#ran","syntology_url":"https://syntology.ai/paper/2303.12501","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.12501"}},"official":{"repos":["anosorae/irra"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/repocoder-repository-level-code-completion","slug":"repocoder-repository-level-code-completion","title":"RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation","date":"2023-03-22","arxiv_id":"2303.12570","repositories_listed":1,"syntology":{"n":7,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":6,"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) · 6 unverified","sample_list":"/paper/repocoder-repository-level-code-completion#ran","syntology_url":"https://syntology.ai/paper/2303.12570","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.12570"}},"official":{"repos":["microsoft/codet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-alignment-of-vision-to-language","slug":"contrastive-alignment-of-vision-to-language","title":"Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning","date":"2023-03-21","arxiv_id":"2303.11866","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":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-alignment-of-vision-to-language#ran","syntology_url":"https://syntology.ai/paper/2303.11866","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11866"}},"official":{"repos":["codezakh/lilt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/implicit-neural-representation-for","slug":"implicit-neural-representation-for","title":"Implicit Neural Representation for Cooperative Low-light Image Enhancement","date":"2023-03-21","arxiv_id":"2303.11722","repositories_listed":1,"syntology":null},{"url":"/paper/language-model-behavior-a-comprehensive","slug":"language-model-behavior-a-comprehensive","title":"Language Model Behavior: A Comprehensive Survey","date":"2023-03-20","arxiv_id":"2303.11504","repositories_listed":1,"syntology":null},{"url":"/paper/clip4mc-an-rl-friendly-vision-language-model","slug":"clip4mc-an-rl-friendly-vision-language-model","title":"Reinforcement Learning Friendly Vision-Language Model for Minecraft","date":"2023-03-19","arxiv_id":"2303.10571","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/clip4mc-an-rl-friendly-vision-language-model#ran","syntology_url":"https://syntology.ai/paper/2303.10571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10571"}},"official":{"repos":["PKU-RL/CLIP4MC"],"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/ctran-cnn-transformer-based-network-for","slug":"ctran-cnn-transformer-based-network-for","title":"CTRAN: CNN-Transformer-based Network for Natural Language Understanding","date":"2023-03-19","arxiv_id":"2303.10606","repositories_listed":1,"syntology":null},{"url":"/paper/is-prompt-all-you-need-no-a-comprehensive-and","slug":"is-prompt-all-you-need-no-a-comprehensive-and","title":"Large Language Model Instruction Following: A Survey of Progresses and Challenges","date":"2023-03-18","arxiv_id":"2303.10475","repositories_listed":1,"syntology":null},{"url":"/paper/champagne-learning-real-world-conversation","slug":"champagne-learning-real-world-conversation","title":"CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos","date":"2023-03-17","arxiv_id":"2303.09713","repositories_listed":1,"syntology":null},{"url":"/paper/logical-implications-for-visual-question","slug":"logical-implications-for-visual-question","title":"Logical Implications for Visual Question Answering Consistency","date":"2023-03-16","arxiv_id":"2303.09427","repositories_listed":1,"syntology":null},{"url":"/paper/typet5-seq2seq-type-inference-using-static","slug":"typet5-seq2seq-type-inference-using-static","title":"TypeT5: Seq2seq Type Inference using Static Analysis","date":"2023-03-16","arxiv_id":"2303.09564","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/typet5-seq2seq-type-inference-using-static#ran","syntology_url":"https://syntology.ai/paper/2303.09564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09564"}},"official":{"repos":["utopia-group/typet5"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/large-language-model-is-not-a-good-few-shot","slug":"large-language-model-is-not-a-good-few-shot","title":"Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!","date":"2023-03-15","arxiv_id":"2303.08559","repositories_listed":1,"syntology":null},{"url":"/paper/lep-ad-language-embedding-of-proteins-and","slug":"lep-ad-language-embedding-of-proteins-and","title":"LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions","date":"2023-03-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/nl4opt-competition-formulating-optimization","slug":"nl4opt-competition-formulating-optimization","title":"NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions","date":"2023-03-14","arxiv_id":"2303.08233","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/nl4opt-competition-formulating-optimization#ran","syntology_url":"https://syntology.ai/paper/2303.08233","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08233"}},"official":{"repos":["nl4opt/nl4opt-competition"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/amom-adaptive-masking-over-masking-for","slug":"amom-adaptive-masking-over-masking-for","title":"AMOM: Adaptive Masking over Masking for Conditional Masked Language Model","date":"2023-03-13","arxiv_id":"2303.07457","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-evaluation-of-chatgpt-s-zero","slug":"a-comprehensive-evaluation-of-chatgpt-s-zero","title":"A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability","date":"2023-03-12","arxiv_id":"2303.13547","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":3,"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: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-comprehensive-evaluation-of-chatgpt-s-zero#ran","syntology_url":"https://syntology.ai/paper/2303.13547","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13547"}},"official":{"repos":["thu-bpm/chatgpt-sql"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/accommodating-audio-modality-in-clip-for","slug":"accommodating-audio-modality-in-clip-for","title":"Accommodating Audio Modality in CLIP for Multimodal Processing","date":"2023-03-12","arxiv_id":"2303.06591","repositories_listed":1,"syntology":null},{"url":"/paper/stabilizing-transformer-training-by","slug":"stabilizing-transformer-training-by","title":"Stabilizing Transformer Training by Preventing Attention Entropy Collapse","date":"2023-03-11","arxiv_id":"2303.06296","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/stabilizing-transformer-training-by#ran","syntology_url":"https://syntology.ai/paper/2303.06296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06296"}},"official":{"repos":["apple/ml-sigma-reparam"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/iterative-few-shot-semantic-segmentation-from","slug":"iterative-few-shot-semantic-segmentation-from","title":"Iterative Few-shot Semantic Segmentation from Image Label Text","date":"2023-03-10","arxiv_id":"2303.05646","repositories_listed":1,"syntology":null}],"record_sha256":"4d60316322a9983364250665a500015d33620ff0619ebc82486ee079fb3d1bd0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}