{"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/natural-language-understanding/papers/5","list_of":"/task/natural-language-understanding","task":"Natural Language Understanding","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":5,"pages_in_order":20,"rows_per_page":100,"rows":[401,500],"of":1978,"counts":{"archive_papers_tagged":1978,"with_a_code_link":809,"where_syntology_ran_a_sample":185,"not_listed_spam_title":0,"listed":1978,"listed_where_code_ran":185,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":155,"every_run_a_failure_of_syntologys_instrument":30,"listed_with_a_run_with_no_instrument_failure":155,"listed_every_run_a_failure_of_syntologys_instrument":30,"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/natural-language-understanding","prev":"/task/natural-language-understanding/papers/4","next":"/task/natural-language-understanding/papers/6","papers":[{"url":"/paper/scene-self-labeled-counterfactuals-for","slug":"scene-self-labeled-counterfactuals-for","title":"SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples","date":"2023-05-13","arxiv_id":"2305.07984","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scene-self-labeled-counterfactuals-for#ran","syntology_url":"https://syntology.ai/paper/2305.07984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07984"}},"official":{"repos":["deqingfu/scene"],"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/autonomous-gis-the-next-generation-ai-powered","slug":"autonomous-gis-the-next-generation-ai-powered","title":"Autonomous GIS: the next-generation AI-powered GIS","date":"2023-05-10","arxiv_id":"2305.06453","repositories_listed":1,"syntology":null},{"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/causality-aware-concept-extraction-based-on","slug":"causality-aware-concept-extraction-based-on","title":"Causality-aware Concept Extraction based on Knowledge-guided Prompting","date":"2023-05-03","arxiv_id":"2305.01876","repositories_listed":1,"syntology":null},{"url":"/paper/chatlog-recording-and-analyzing-chatgpt","slug":"chatlog-recording-and-analyzing-chatgpt","title":"ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time","date":"2023-04-27","arxiv_id":"2304.14106","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chatlog-recording-and-analyzing-chatgpt#ran","syntology_url":"https://syntology.ai/paper/2304.14106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.14106"}},"official":{"repos":["thu-keg/chatlog"],"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/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/harnessing-the-power-of-llms-in-practice-a","slug":"harnessing-the-power-of-llms-in-practice-a","title":"Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond","date":"2023-04-26","arxiv_id":"2304.13712","repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-bilingual-dialect-lexicon","slug":"low-resource-bilingual-dialect-lexicon","title":"Low-resource Bilingual Dialect Lexicon Induction with Large Language Models","date":"2023-04-19","arxiv_id":"2304.09957","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-state-of-the-art-in-legal-qa","slug":"exploring-the-state-of-the-art-in-legal-qa","title":"Exploring the State of the Art in Legal QA Systems","date":"2023-04-13","arxiv_id":"2304.06623","repositories_listed":1,"syntology":null},{"url":"/paper/towards-preserving-word-order-importance","slug":"towards-preserving-word-order-importance","title":"Towards preserving word order importance through Forced Invalidation","date":"2023-04-11","arxiv_id":"2304.05221","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/evaluating-the-logical-reasoning-ability-of","slug":"evaluating-the-logical-reasoning-ability-of","title":"Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4","date":"2023-04-07","arxiv_id":"2304.03439","repositories_listed":1,"syntology":null},{"url":"/paper/unleashing-the-power-of-chatgpt-for","slug":"unleashing-the-power-of-chatgpt-for","title":"How to Design Translation Prompts for ChatGPT: An Empirical Study","date":"2023-04-05","arxiv_id":"2304.02182","repositories_listed":1,"syntology":null},{"url":"/paper/form-nlu-dataset-for-the-form-language","slug":"form-nlu-dataset-for-the-form-language","title":"Form-NLU: Dataset for the Form Natural Language Understanding","date":"2023-04-04","arxiv_id":"2304.01577","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/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/error-analysis-prompting-enables-human-like","slug":"error-analysis-prompting-enables-human-like","title":"Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models","date":"2023-03-24","arxiv_id":"2303.13809","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/is-bert-blind-exploring-the-effect-of-vision","slug":"is-bert-blind-exploring-the-effect-of-vision","title":"Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding","date":"2023-03-21","arxiv_id":"2303.12513","repositories_listed":1,"syntology":null},{"url":"/paper/capabilities-of-gpt-4-on-medical-challenge","slug":"capabilities-of-gpt-4-on-medical-challenge","title":"Capabilities of GPT-4 on Medical Challenge Problems","date":"2023-03-20","arxiv_id":"2303.13375","repositories_listed":1,"syntology":null},{"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/a-hybrid-architecture-for-out-of-domain","slug":"a-hybrid-architecture-for-out-of-domain","title":"A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery","date":"2023-03-07","arxiv_id":"2303.04134","repositories_listed":1,"syntology":null},{"url":"/paper/mathprompter-mathematical-reasoning-using","slug":"mathprompter-mathematical-reasoning-using","title":"MathPrompter: Mathematical Reasoning using Large Language Models","date":"2023-03-04","arxiv_id":"2303.05398","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mathprompter-mathematical-reasoning-using#ran","syntology_url":"https://syntology.ai/paper/2303.05398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.05398"}},"official":null}},{"url":"/paper/knowledge-enhanced-pre-training-for-auto","slug":"knowledge-enhanced-pre-training-for-auto","title":"Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images","date":"2023-02-27","arxiv_id":"2302.14042","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":3,"n_no_contract":3,"n_pointer_only":6,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 3 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/knowledge-enhanced-pre-training-for-auto#ran","syntology_url":"https://syntology.ai/paper/2302.14042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14042"}},"official":null}},{"url":"/paper/is-multi-modal-vision-supervision-beneficial","slug":"is-multi-modal-vision-supervision-beneficial","title":"Is Multimodal Vision Supervision Beneficial to Language?","date":"2023-02-10","arxiv_id":"2302.05016","repositories_listed":1,"syntology":null},{"url":"/paper/gladis-a-general-and-large-acronym","slug":"gladis-a-general-and-large-acronym","title":"GLADIS: A General and Large Acronym Disambiguation Benchmark","date":"2023-02-03","arxiv_id":"2302.01860","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 0 unverified","sample_list":"/paper/gladis-a-general-and-large-acronym#ran","syntology_url":"https://syntology.ai/paper/2302.01860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01860"}},"official":{"repos":["tigerchen52/gladis"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/call-for-papers-the-babylm-challenge-sample","slug":"call-for-papers-the-babylm-challenge-sample","title":"Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus","date":"2023-01-27","arxiv_id":"2301.11796","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/call-for-papers-the-babylm-challenge-sample#ran","syntology_url":"https://syntology.ai/paper/2301.11796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11796"}},"official":null}},{"url":"/paper/videberta-a-powerful-pre-trained-language","slug":"videberta-a-powerful-pre-trained-language","title":"ViDeBERTa: A powerful pre-trained language model for Vietnamese","date":"2023-01-25","arxiv_id":"2301.10439","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/videberta-a-powerful-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2301.10439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10439"}},"official":{"repos":["hysonlab/videberta"],"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/neural-architecture-search-insights-from-1000","slug":"neural-architecture-search-insights-from-1000","title":"Neural Architecture Search: Insights from 1000 Papers","date":"2023-01-20","arxiv_id":"2301.08727","repositories_listed":1,"syntology":null},{"url":"/paper/cross-model-comparative-loss-for-enhancing","slug":"cross-model-comparative-loss-for-enhancing","title":"Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding","date":"2023-01-10","arxiv_id":"2301.03765","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-as-corporate-lobbyists","slug":"large-language-models-as-corporate-lobbyists","title":"Large Language Models as Corporate Lobbyists","date":"2023-01-03","arxiv_id":"2301.01181","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-encode-clinical","slug":"large-language-models-encode-clinical","title":"Large Language Models Encode Clinical Knowledge","date":"2022-12-26","arxiv_id":"2212.13138","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-fly-denoising-for-data-augmentation-in","slug":"on-the-fly-denoising-for-data-augmentation-in","title":"On-the-fly Denoising for Data Augmentation in Natural Language Understanding","date":"2022-12-20","arxiv_id":"2212.10558","repositories_listed":1,"syntology":null},{"url":"/paper/plue-language-understanding-evaluation","slug":"plue-language-understanding-evaluation","title":"PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English","date":"2022-12-20","arxiv_id":"2212.10011","repositories_listed":1,"syntology":null},{"url":"/paper/nusacrowd-open-source-initiative-for","slug":"nusacrowd-open-source-initiative-for","title":"NusaCrowd: Open Source Initiative for Indonesian NLP Resources","date":"2022-12-19","arxiv_id":"2212.09648","repositories_listed":1,"syntology":null},{"url":"/paper/convolution-enhanced-evolving-attention","slug":"convolution-enhanced-evolving-attention","title":"Convolution-enhanced Evolving Attention Networks","date":"2022-12-16","arxiv_id":"2212.08330","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-long-sequence-modeling-via-state","slug":"efficient-long-sequence-modeling-via-state","title":"Efficient Long Sequence Modeling via State Space Augmented Transformer","date":"2022-12-15","arxiv_id":"2212.08136","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/efficient-long-sequence-modeling-via-state#ran","syntology_url":"https://syntology.ai/paper/2212.08136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08136"}},"official":{"repos":["microsoft/efficientlongsequencemodeling"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/image-and-language-understanding-from-pixels","slug":"image-and-language-understanding-from-pixels","title":"CLIPPO: Image-and-Language Understanding from Pixels Only","date":"2022-12-15","arxiv_id":"2212.08045","repositories_listed":1,"syntology":null},{"url":"/paper/the-kitmus-test-evaluating-knowledge","slug":"the-kitmus-test-evaluating-knowledge","title":"The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources in Natural Language Understanding Systems","date":"2022-12-15","arxiv_id":"2212.08192","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":3,"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/the-kitmus-test-evaluating-knowledge#ran","syntology_url":"https://syntology.ai/paper/2212.08192","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.08192"}},"official":{"repos":["mpoemsl/kitmus"],"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/collaborating-heterogeneous-natural-language","slug":"collaborating-heterogeneous-natural-language","title":"Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning","date":"2022-12-12","arxiv_id":"2212.05789","repositories_listed":1,"syntology":null},{"url":"/paper/rpn-a-word-vector-level-data-augmentation","slug":"rpn-a-word-vector-level-data-augmentation","title":"RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding","date":"2022-12-12","arxiv_id":"2212.05961","repositories_listed":1,"syntology":null},{"url":"/paper/feature-level-debiased-natural-language","slug":"feature-level-debiased-natural-language","title":"Feature-Level Debiased Natural Language Understanding","date":"2022-12-11","arxiv_id":"2212.05421","repositories_listed":1,"syntology":null},{"url":"/paper/indicxtreme-a-multi-task-benchmark-for","slug":"indicxtreme-a-multi-task-benchmark-for","title":"Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages","date":"2022-12-11","arxiv_id":"2212.05409","repositories_listed":1,"syntology":null},{"url":"/paper/narrasum-a-large-scale-dataset-for","slug":"narrasum-a-large-scale-dataset-for","title":"NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization","date":"2022-12-02","arxiv_id":"2212.01476","repositories_listed":1,"syntology":null},{"url":"/paper/gpt-neo-for-commonsense-reasoning-a","slug":"gpt-neo-for-commonsense-reasoning-a","title":"GPT-Neo for commonsense reasoning -- a theoretical and practical lens","date":"2022-11-28","arxiv_id":"2211.15593","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-sarcastic-utterances-to-enhance","slug":"explaining-sarcastic-utterances-to-enhance","title":"Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues","date":"2022-11-20","arxiv_id":"2211.11049","repositories_listed":1,"syntology":null},{"url":"/paper/breakpoint-transformers-for-modeling-and","slug":"breakpoint-transformers-for-modeling-and","title":"Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs","date":"2022-11-15","arxiv_id":"2211.07950","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/breakpoint-transformers-for-modeling-and#ran","syntology_url":"https://syntology.ai/paper/2211.07950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.07950"}},"official":{"repos":["allenai/situation_modeling"],"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/glue-x-evaluating-natural-language","slug":"glue-x-evaluating-natural-language","title":"GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective","date":"2022-11-15","arxiv_id":"2211.08073","repositories_listed":1,"syntology":null},{"url":"/paper/condaqa-a-contrastive-reading-comprehension","slug":"condaqa-a-contrastive-reading-comprehension","title":"CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation","date":"2022-11-01","arxiv_id":"2211.00295","repositories_listed":1,"syntology":null},{"url":"/paper/parameter-efficient-tuning-makes-a-good","slug":"parameter-efficient-tuning-makes-a-good","title":"Parameter-Efficient Tuning Makes a Good Classification Head","date":"2022-10-30","arxiv_id":"2210.16771","repositories_listed":1,"syntology":null},{"url":"/paper/debiasing-masks-a-new-framework-for-shortcut","slug":"debiasing-masks-a-new-framework-for-shortcut","title":"Debiasing Masks: A New Framework for Shortcut Mitigation in NLU","date":"2022-10-28","arxiv_id":"2210.16079","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-multi-task-learning-for-abstractive","slug":"analyzing-multi-task-learning-for-abstractive","title":"Analyzing Multi-Task Learning for Abstractive Text Summarization","date":"2022-10-26","arxiv_id":"2210.14606","repositories_listed":1,"syntology":null},{"url":"/paper/inducer-tuning-connecting-prefix-tuning-and","slug":"inducer-tuning-connecting-prefix-tuning-and","title":"Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning","date":"2022-10-26","arxiv_id":"2210.14469","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-affirmative-interpretations-from","slug":"leveraging-affirmative-interpretations-from","title":"Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding","date":"2022-10-26","arxiv_id":"2210.14486","repositories_listed":1,"syntology":null},{"url":"/paper/idk-mrc-unanswerable-questions-for-indonesian","slug":"idk-mrc-unanswerable-questions-for-indonesian","title":"IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension","date":"2022-10-25","arxiv_id":"2210.13778","repositories_listed":1,"syntology":null},{"url":"/paper/expunations-augmenting-puns-with-keywords-and","slug":"expunations-augmenting-puns-with-keywords-and","title":"ExPUNations: Augmenting Puns with Keywords and Explanations","date":"2022-10-24","arxiv_id":"2210.13513","repositories_listed":1,"syntology":null},{"url":"/paper/training-dynamics-for-curriculum-learning-a-1","slug":"training-dynamics-for-curriculum-learning-a-1","title":"Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU","date":"2022-10-22","arxiv_id":"2210.12499","repositories_listed":1,"syntology":null},{"url":"/paper/informask-unsupervised-informative-masking","slug":"informask-unsupervised-informative-masking","title":"InforMask: Unsupervised Informative Masking for Language Model Pretraining","date":"2022-10-21","arxiv_id":"2210.11771","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/informask-unsupervised-informative-masking#ran","syntology_url":"https://syntology.ai/paper/2210.11771","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11771"}},"official":{"repos":["nafissadeq/informask"],"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/enhancing-out-of-distribution-detection-in","slug":"enhancing-out-of-distribution-detection-in","title":"Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble","date":"2022-10-20","arxiv_id":"2210.11034","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-out-of-distribution-detection-in#ran","syntology_url":"https://syntology.ai/paper/2210.11034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11034"}},"official":{"repos":["hyunsoocho77/lacl-official"],"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/language-detoxification-with-attribute","slug":"language-detoxification-with-attribute","title":"Language Detoxification with Attribute-Discriminative Latent Space","date":"2022-10-19","arxiv_id":"2210.10329","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-prompting-in-pre-trained-language","slug":"knowledge-prompting-in-pre-trained-language","title":"Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding","date":"2022-10-16","arxiv_id":"2210.08536","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learners-for-natural-language","slug":"zero-shot-learners-for-natural-language","title":"Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective","date":"2022-10-16","arxiv_id":"2210.08590","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-conditioned-vae-enhancing-generative","slug":"prompt-conditioned-vae-enhancing-generative","title":"Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue","date":"2022-10-14","arxiv_id":"2210.07783","repositories_listed":1,"syntology":null},{"url":"/paper/a-win-win-deal-towards-sparse-and-robust-pre","slug":"a-win-win-deal-towards-sparse-and-robust-pre","title":"A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models","date":"2022-10-11","arxiv_id":"2210.05211","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":2,"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/a-win-win-deal-towards-sparse-and-robust-pre#ran","syntology_url":"https://syntology.ai/paper/2210.05211","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.05211"}},"official":{"repos":["llyx97/sparse-and-robust-plm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-regularization-for-discriminative","slug":"instance-regularization-for-discriminative","title":"Instance Regularization for Discriminative Language Model Pre-training","date":"2022-10-11","arxiv_id":"2210.05471","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-and-advancing-chinese-natural","slug":"revisiting-and-advancing-chinese-natural","title":"Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training","date":"2022-10-11","arxiv_id":"2210.05287","repositories_listed":1,"syntology":null},{"url":"/paper/parameter-efficient-tuning-with-special-token","slug":"parameter-efficient-tuning-with-special-token","title":"Parameter-Efficient Tuning with Special Token Adaptation","date":"2022-10-10","arxiv_id":"2210.04382","repositories_listed":1,"syntology":null},{"url":"/paper/aligning-multilingual-embeddings-for-improved","slug":"aligning-multilingual-embeddings-for-improved","title":"Aligning Multilingual Embeddings for Improved Code-switched Natural Language Understanding","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/are-visual-linguistic-models-commonsense","slug":"are-visual-linguistic-models-commonsense","title":"Are Visual-Linguistic Models Commonsense Knowledge Bases?","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-coreference-resolvers-on-community","slug":"evaluating-coreference-resolvers-on-community","title":"Evaluating Coreference Resolvers on Community-based Question Answering: From Rule-based to State of the Art","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-natural-language-generation-for-task","slug":"adaptive-natural-language-generation-for-task","title":"Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning","date":"2022-09-16","arxiv_id":"2209.07873","repositories_listed":1,"syntology":null},{"url":"/paper/belief-revision-based-caption-re-ranker-with","slug":"belief-revision-based-caption-re-ranker-with","title":"Belief Revision based Caption Re-ranker with Visual Semantic Information","date":"2022-09-16","arxiv_id":"2209.08163","repositories_listed":1,"syntology":null},{"url":"/paper/configure-exploring-discourse-level-chinese","slug":"configure-exploring-discourse-level-chinese","title":"ConFiguRe: Exploring Discourse-level Chinese Figures of Speech","date":"2022-09-16","arxiv_id":"2209.07678","repositories_listed":1,"syntology":null},{"url":"/paper/multi-grained-label-refinement-network-with","slug":"multi-grained-label-refinement-network-with","title":"Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling","date":"2022-09-09","arxiv_id":"2209.04156","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-machine-learning-models-in-natural","slug":"explaining-machine-learning-models-in-natural","title":"From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent","date":"2022-09-06","arxiv_id":"2209.02552","repositories_listed":1,"syntology":null},{"url":"/paper/semantically-meaningful-metrics-for-norwegian","slug":"semantically-meaningful-metrics-for-norwegian","title":"Semantically Meaningful Metrics for Norwegian ASR Systems","date":"2022-09-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/folio-natural-language-reasoning-with-first","slug":"folio-natural-language-reasoning-with-first","title":"FOLIO: Natural Language Reasoning with First-Order Logic","date":"2022-09-02","arxiv_id":"2209.00840","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-n-best-calibration-of-natural","slug":"evaluating-n-best-calibration-of-natural","title":"Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems","date":"2022-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/indicsuperb-a-speech-processing-universal","slug":"indicsuperb-a-speech-processing-universal","title":"IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages","date":"2022-08-24","arxiv_id":"2208.11761","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/indicsuperb-a-speech-processing-universal#ran","syntology_url":"https://syntology.ai/paper/2208.11761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.11761"}},"official":{"repos":["AI4Bharat/indicSUPERB"],"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/efficient-long-text-understanding-with-short","slug":"efficient-long-text-understanding-with-short","title":"Efficient Long-Text Understanding with Short-Text Models","date":"2022-08-01","arxiv_id":"2208.00748","repositories_listed":1,"syntology":null},{"url":"/paper/pic-a-phrase-in-context-dataset-for-phrase","slug":"pic-a-phrase-in-context-dataset-for-phrase","title":"PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search","date":"2022-07-19","arxiv_id":"2207.09068","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-translate-by-learning-to","slug":"learning-to-translate-by-learning-to","title":"Learning to translate by learning to communicate","date":"2022-07-14","arxiv_id":"2207.07025","repositories_listed":1,"syntology":null},{"url":"/paper/alexu-al-at-semeval-2022-task-6-detecting","slug":"alexu-al-at-semeval-2022-task-6-detecting","title":"AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/id10m-idiom-identification-in-10-languages","slug":"id10m-idiom-identification-in-10-languages","title":"ID10M: Idiom Identification in 10 Languages","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/is-neural-language-acquisition-similar-to","slug":"is-neural-language-acquisition-similar-to","title":"Is neural language acquisition similar to natural? A chronological probing study","date":"2022-07-01","arxiv_id":"2207.00560","repositories_listed":1,"syntology":null},{"url":"/paper/ner4id-at-semeval-2022-task-2-named-entity","slug":"ner4id-at-semeval-2022-task-2-named-entity","title":"NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-curriculum-learning-for-commonsense","slug":"on-curriculum-learning-for-commonsense","title":"On Curriculum Learning for Commonsense Reasoning","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/solving-quantitative-reasoning-problems-with","slug":"solving-quantitative-reasoning-problems-with","title":"Solving Quantitative Reasoning Problems with Language Models","date":"2022-06-29","arxiv_id":"2206.14858","repositories_listed":1,"syntology":null},{"url":"/paper/zodiac-zoneout-dropout-injection-attention","slug":"zodiac-zoneout-dropout-injection-attention","title":"ZoDIAC: Zoneout Dropout Injection Attention Calculation","date":"2022-06-28","arxiv_id":"2206.14263","repositories_listed":1,"syntology":null},{"url":"/paper/endowing-language-models-with-multimodal","slug":"endowing-language-models-with-multimodal","title":"Endowing Language Models with Multimodal Knowledge Graph Representations","date":"2022-06-27","arxiv_id":"2206.13163","repositories_listed":1,"syntology":null},{"url":"/paper/platon-pruning-large-transformer-models-with","slug":"platon-pruning-large-transformer-models-with","title":"PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance","date":"2022-06-25","arxiv_id":"2206.12562","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/platon-pruning-large-transformer-models-with#ran","syntology_url":"https://syntology.ai/paper/2206.12562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.12562"}},"official":{"repos":["qingruzhang/platon"],"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","unlocated"]}}},{"url":"/paper/chq-summ-a-dataset-for-consumer-healthcare","slug":"chq-summ-a-dataset-for-consumer-healthcare","title":"CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization","date":"2022-06-14","arxiv_id":"2206.06581","repositories_listed":1,"syntology":null},{"url":"/paper/tce-at-qur-an-qa-2022-arabic-language","slug":"tce-at-qur-an-qa-2022-arabic-language","title":"TCE at Qur'an QA 2022: Arabic Language Question Answering Over Holy Qur'an Using a Post-Processed Ensemble of BERT-based Models","date":"2022-06-03","arxiv_id":"2206.01550","repositories_listed":1,"syntology":null},{"url":"/paper/basqueglue-a-natural-language-understanding","slug":"basqueglue-a-natural-language-understanding","title":"BasqueGLUE: A Natural Language Understanding Benchmark for Basque","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dtw-at-quran-qa-2022-utilising-transfer","slug":"dtw-at-quran-qa-2022-utilising-transfer","title":"DTW at Qur’an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tce-at-quran-qa-2022-arabic-language-question","slug":"tce-at-quran-qa-2022-arabic-language-question","title":"TCE at Qur’an QA 2022: Arabic Language Question Answering Over Holy Qur’an Using a Post-Processed Ensemble of BERT-based Models","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-robotic-surgery-procedural-framebank","slug":"the-robotic-surgery-procedural-framebank","title":"The Robotic Surgery Procedural Framebank","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/e2s2-encoding-enhanced-sequence-to-sequence","slug":"e2s2-encoding-enhanced-sequence-to-sequence","title":"E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation","date":"2022-05-30","arxiv_id":"2205.14912","repositories_listed":1,"syntology":null},{"url":"/paper/iglu-2022-interactive-grounded-language","slug":"iglu-2022-interactive-grounded-language","title":"IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022","date":"2022-05-27","arxiv_id":"2205.13771","repositories_listed":1,"syntology":null},{"url":"/paper/gispy-a-tool-for-measuring-gist-inference","slug":"gispy-a-tool-for-measuring-gist-inference","title":"GisPy: A Tool for Measuring Gist Inference Score in Text","date":"2022-05-25","arxiv_id":"2205.12484","repositories_listed":1,"syntology":null}],"record_sha256":"a2f724f906dcef69f7a910da2dd5a6fa70c2f921da3e4b2d040bb7523ba02dfc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}