{"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/2","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":2,"pages_in_order":20,"rows_per_page":100,"rows":[101,200],"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","next":"/task/natural-language-understanding/papers/3","papers":[{"url":"/paper/prompt-tuning-can-be-much-better-than-fine","slug":"prompt-tuning-can-be-much-better-than-fine","title":"Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models","date":"2022-10-22","arxiv_id":"2210.12360","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/prompt-tuning-can-be-much-better-than-fine#ran","syntology_url":"https://syntology.ai/paper/2210.12360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12360"}},"official":{"repos":["salesforce/mpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dylora-parameter-efficient-tuning-of-pre","slug":"dylora-parameter-efficient-tuning-of-pre","title":"DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation","date":"2022-10-14","arxiv_id":"2210.07558","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/dylora-parameter-efficient-tuning-of-pre#ran","syntology_url":"https://syntology.ai/paper/2210.07558","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07558"}},"official":{"repos":["huawei-noah/kd-nlp"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/explaining-patterns-in-data-with-language","slug":"explaining-patterns-in-data-with-language","title":"Explaining Patterns in Data with Language Models via Interpretable Autoprompting","date":"2022-10-04","arxiv_id":"2210.01848","repositories_listed":2,"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/explaining-patterns-in-data-with-language#ran","syntology_url":"https://syntology.ai/paper/2210.01848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01848"}},"official":{"repos":["csinva/imodelsX"],"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/plm-icd-automatic-icd-coding-with-pretrained-1","slug":"plm-icd-automatic-icd-coding-with-pretrained-1","title":"PLM-ICD: Automatic ICD Coding with Pretrained Language Models","date":"2022-07-12","arxiv_id":"2207.05289","repositories_listed":2,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/plm-icd-automatic-icd-coding-with-pretrained-1#ran","syntology_url":"https://syntology.ai/paper/2207.05289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.05289"}},"official":{"repos":["miulab/plm-icd"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/jglue-japanese-general-language-understanding","slug":"jglue-japanese-general-language-understanding","title":"JGLUE: Japanese General Language Understanding Evaluation","date":"2022-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/albeto-and-distilbeto-lightweight-spanish","slug":"albeto-and-distilbeto-lightweight-spanish","title":"ALBETO and DistilBETO: Lightweight Spanish Language Models","date":"2022-04-19","arxiv_id":"2204.09145","repositories_listed":2,"syntology":null},{"url":"/paper/a-neural-symbolic-approach-to-natural","slug":"a-neural-symbolic-approach-to-natural","title":"A Neural-Symbolic Approach to Natural Language Understanding","date":"2022-03-20","arxiv_id":"2203.10557","repositories_listed":2,"syntology":null},{"url":"/paper/hypermixer-an-mlp-based-green-ai-alternative","slug":"hypermixer-an-mlp-based-green-ai-alternative","title":"HyperMixer: An MLP-based Low Cost Alternative to Transformers","date":"2022-03-07","arxiv_id":"2203.03691","repositories_listed":2,"syntology":null},{"url":"/paper/tableformer-robust-transformer-modeling-for-1","slug":"tableformer-robust-transformer-modeling-for-1","title":"TableFormer: Robust Transformer Modeling for Table-Text Encoding","date":"2022-03-01","arxiv_id":"2203.00274","repositories_listed":2,"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/tableformer-robust-transformer-modeling-for-1#ran","syntology_url":"https://syntology.ai/paper/2203.00274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.00274"}},"official":{"repos":["google-research/tapas"],"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/decorrelate-irrelevant-purify-relevant","slug":"decorrelate-irrelevant-purify-relevant","title":"Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective","date":"2022-02-16","arxiv_id":"2202.08048","repositories_listed":2,"syntology":null},{"url":"/paper/learning-to-retrieve-prompts-for-in-context","slug":"learning-to-retrieve-prompts-for-in-context","title":"Learning To Retrieve Prompts for In-Context Learning","date":"2021-12-16","arxiv_id":"2112.08633","repositories_listed":2,"syntology":null},{"url":"/paper/tacl-improving-bert-pre-training-with-token","slug":"tacl-improving-bert-pre-training-with-token","title":"TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning","date":"2021-11-07","arxiv_id":"2111.04198","repositories_listed":2,"syntology":null},{"url":"/paper/federated-distillation-of-natural-language","slug":"federated-distillation-of-natural-language","title":"KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks","date":"2021-10-06","arxiv_id":"2110.02432","repositories_listed":2,"syntology":null},{"url":"/paper/call-larisa-ivanovna-code-switching-fools","slug":"call-larisa-ivanovna-code-switching-fools","title":"Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models","date":"2021-09-29","arxiv_id":"2109.14350","repositories_listed":2,"syntology":null},{"url":"/paper/debiasing-methods-in-natural-language","slug":"debiasing-methods-in-natural-language","title":"Debiasing Methods in Natural Language Understanding Make Bias More Accessible","date":"2021-09-09","arxiv_id":"2109.04095","repositories_listed":2,"syntology":null},{"url":"/paper/creak-a-dataset-for-commonsense-reasoning","slug":"creak-a-dataset-for-commonsense-reasoning","title":"CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge","date":"2021-09-03","arxiv_id":"2109.01653","repositories_listed":2,"syntology":null},{"url":"/paper/ernie-3-0-large-scale-knowledge-enhanced-pre","slug":"ernie-3-0-large-scale-knowledge-enhanced-pre","title":"ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation","date":"2021-07-05","arxiv_id":"2107.02137","repositories_listed":2,"syntology":null},{"url":"/paper/faviq-fact-verification-from-information","slug":"faviq-fact-verification-from-information","title":"FaVIQ: FAct Verification from Information-seeking Questions","date":"2021-07-05","arxiv_id":"2107.02153","repositories_listed":2,"syntology":null},{"url":"/paper/deltalm-encoder-decoder-pre-training-for","slug":"deltalm-encoder-decoder-pre-training-for","title":"DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders","date":"2021-06-25","arxiv_id":"2106.13736","repositories_listed":2,"syntology":null},{"url":"/paper/a-generative-symbolic-model-for-more-general","slug":"a-generative-symbolic-model-for-more-general","title":"Towards General Natural Language Understanding with Probabilistic Worldbuilding","date":"2021-05-06","arxiv_id":"2105.02486","repositories_listed":2,"syntology":{"n":14,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":7,"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, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/a-generative-symbolic-model-for-more-general#ran","syntology_url":"https://syntology.ai/paper/2105.02486","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.02486"}},"official":{"repos":["asaparov/fictionalgeoqa","asaparov/pwl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/xeroalign-zero-shot-cross-lingual-transformer","slug":"xeroalign-zero-shot-cross-lingual-transformer","title":"XeroAlign: Zero-Shot Cross-lingual Transformer Alignment","date":"2021-05-06","arxiv_id":"2105.02472","repositories_listed":2,"syntology":null},{"url":"/paper/scico-hierarchical-cross-document-coreference","slug":"scico-hierarchical-cross-document-coreference","title":"SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts","date":"2021-04-18","arxiv_id":"2104.08809","repositories_listed":2,"syntology":null},{"url":"/paper/lattice-bert-leveraging-multi-granularity","slug":"lattice-bert-leveraging-multi-granularity","title":"Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models","date":"2021-04-15","arxiv_id":"2104.07204","repositories_listed":2,"syntology":null},{"url":"/paper/learning-chess-blindfolded-evaluating","slug":"learning-chess-blindfolded-evaluating","title":"Chess as a Testbed for Language Model State Tracking","date":"2021-02-26","arxiv_id":"2102.13249","repositories_listed":2,"syntology":null},{"url":"/paper/evolving-attention-with-residual-convolutions","slug":"evolving-attention-with-residual-convolutions","title":"Evolving Attention with Residual Convolutions","date":"2021-02-20","arxiv_id":"2102.12895","repositories_listed":2,"syntology":null},{"url":"/paper/textgnn-improving-text-encoder-via-graph","slug":"textgnn-improving-text-encoder-via-graph","title":"TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search","date":"2021-01-15","arxiv_id":"2101.06323","repositories_listed":2,"syntology":null},{"url":"/paper/confet-an-english-sentence-to-emojis","slug":"confet-an-english-sentence-to-emojis","title":"CoNFET: An English Sentence to Emojis Translation Algorithm","date":"2021-01-06","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/robustness-testing-of-language-understanding","slug":"robustness-testing-of-language-understanding","title":"Robustness Testing of Language Understanding in Task-Oriented Dialog","date":"2020-12-30","arxiv_id":"2012.15262","repositories_listed":2,"syntology":null},{"url":"/paper/comparison-by-conversion-reverse-engineering","slug":"comparison-by-conversion-reverse-engineering","title":"Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics","date":"2020-11-02","arxiv_id":"2011.00834","repositories_listed":2,"syntology":null},{"url":"/paper/russiansuperglue-a-russian-language","slug":"russiansuperglue-a-russian-language","title":"RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark","date":"2020-10-29","arxiv_id":"2010.15925","repositories_listed":2,"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/russiansuperglue-a-russian-language#ran","syntology_url":"https://syntology.ai/paper/2010.15925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.15925"}},"official":{"repos":["RussianNLP/RussianSuperGLUE"],"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/fastformers-highly-efficient-transformer","slug":"fastformers-highly-efficient-transformer","title":"FastFormers: Highly Efficient Transformer Models for Natural Language Understanding","date":"2020-10-26","arxiv_id":"2010.13382","repositories_listed":2,"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/fastformers-highly-efficient-transformer#ran","syntology_url":"https://syntology.ai/paper/2010.13382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.13382"}},"official":{"repos":["microsoft/fastformers"],"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/hierarchical-transformer-for-task-oriented","slug":"hierarchical-transformer-for-task-oriented","title":"Hierarchical Transformer for Task Oriented Dialog Systems","date":"2020-10-24","arxiv_id":"2011.08067","repositories_listed":2,"syntology":null},{"url":"/paper/ernie-gram-pre-training-with-explicitly-n","slug":"ernie-gram-pre-training-with-explicitly-n","title":"ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding","date":"2020-10-23","arxiv_id":"2010.12148","repositories_listed":2,"syntology":null},{"url":"/paper/a-simple-but-tough-to-beat-data-augmentation","slug":"a-simple-but-tough-to-beat-data-augmentation","title":"A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation","date":"2020-09-29","arxiv_id":"2009.13818","repositories_listed":2,"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/a-simple-but-tough-to-beat-data-augmentation#ran","syntology_url":"https://syntology.ai/paper/2009.13818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13818"}},"official":{"repos":["dinghanshen/Cutoff"],"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/logiqa-a-challenge-dataset-for-machine","slug":"logiqa-a-challenge-dataset-for-machine","title":"LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning","date":"2020-07-16","arxiv_id":"2007.08124","repositories_listed":2,"syntology":null},{"url":"/paper/advances-of-transformer-based-models-for-news","slug":"advances-of-transformer-based-models-for-news","title":"Advances of Transformer-Based Models for News Headline Generation","date":"2020-07-09","arxiv_id":"2007.05044","repositories_listed":2,"syntology":null},{"url":"/paper/lexical-semantic-recognition","slug":"lexical-semantic-recognition","title":"Lexical Semantic Recognition","date":"2020-04-30","arxiv_id":"2004.15008","repositories_listed":2,"syntology":null},{"url":"/paper/dual-learning-for-semi-supervised-natural","slug":"dual-learning-for-semi-supervised-natural","title":"Dual Learning for Semi-Supervised Natural Language Understanding","date":"2020-04-26","arxiv_id":"2004.12299","repositories_listed":2,"syntology":null},{"url":"/paper/palm-pre-training-an-autoencoding","slug":"palm-pre-training-an-autoencoding","title":"PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation","date":"2020-04-14","arxiv_id":"2004.07159","repositories_listed":2,"syntology":null},{"url":"/paper/xglue-a-new-benchmark-dataset-for-cross","slug":"xglue-a-new-benchmark-dataset-for-cross","title":"XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation","date":"2020-04-03","arxiv_id":"2004.01401","repositories_listed":2,"syntology":null},{"url":"/paper/pre-trained-contextual-embedding-of-source-1","slug":"pre-trained-contextual-embedding-of-source-1","title":"Learning and Evaluating Contextual Embedding of Source Code","date":"2019-12-21","arxiv_id":"2001.00059","repositories_listed":2,"syntology":null},{"url":"/paper/enriching-existing-conversational-emotion","slug":"enriching-existing-conversational-emotion","title":"EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators","date":"2019-12-02","arxiv_id":"1912.00819","repositories_listed":2,"syntology":null},{"url":"/paper/piqa-reasoning-about-physical-commonsense-in","slug":"piqa-reasoning-about-physical-commonsense-in","title":"PIQA: Reasoning about Physical Commonsense in Natural Language","date":"2019-11-26","arxiv_id":"1911.11641","repositories_listed":2,"syntology":null},{"url":"/paper/adversarial-nli-a-new-benchmark-for-natural","slug":"adversarial-nli-a-new-benchmark-for-natural","title":"Adversarial NLI: A New Benchmark for Natural Language Understanding","date":"2019-10-31","arxiv_id":"1910.14599","repositories_listed":2,"syntology":{"n":12,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":8,"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) · 8 unverified","sample_list":"/paper/adversarial-nli-a-new-benchmark-for-natural#ran","syntology_url":"https://syntology.ai/paper/1910.14599","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.14599"}},"official":{"repos":["facebookresearch/anli"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/freelb-enhanced-adversarial-training-for","slug":"freelb-enhanced-adversarial-training-for","title":"FreeLB: Enhanced Adversarial Training for Natural Language Understanding","date":"2019-09-25","arxiv_id":"1909.11764","repositories_listed":2,"syntology":null},{"url":"/paper/simple-but-effective-techniques-to-reduce","slug":"simple-but-effective-techniques-to-reduce","title":"End-to-End Bias Mitigation by Modelling Biases in Corpora","date":"2019-09-13","arxiv_id":"1909.06321","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/simple-but-effective-techniques-to-reduce#ran","syntology_url":"https://syntology.ai/paper/1909.06321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.06321"}},"official":{"repos":["rabeehk/robust-nli","rabeehk/robust-nli-fixed"],"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/moss-end-to-end-dialog-system-framework-with","slug":"moss-end-to-end-dialog-system-framework-with","title":"MOSS: End-to-End Dialog System Framework with Modular Supervision","date":"2019-09-12","arxiv_id":"1909.05528","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moss-end-to-end-dialog-system-framework-with#ran","syntology_url":"https://syntology.ai/paper/1909.05528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.05528"}},"official":null}},{"url":"/paper/190807898","slug":"190807898","title":"Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets","date":"2019-08-21","arxiv_id":"1908.07898","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/190807898#ran","syntology_url":"https://syntology.ai/paper/1908.07898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.07898"}},"official":null}},{"url":"/paper/discourse-based-evaluation-of-language","slug":"discourse-based-evaluation-of-language","title":"A Pragmatics-Centered Evaluation Framework for Natural Language Understanding","date":"2019-07-19","arxiv_id":"1907.08672","repositories_listed":2,"syntology":null},{"url":"/paper/190600363","slug":"190600363","title":"Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation","date":"2019-06-02","arxiv_id":"1906.00363","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/190600363#ran","syntology_url":"https://syntology.ai/paper/1906.00363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.00363"}},"official":{"repos":["wangcunxiang/Sen-Making-and-Explanation"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/190600138","slug":"190600138","title":"Efficient Adaptation of Pretrained Transformers for Abstractive Summarization","date":"2019-06-01","arxiv_id":"1906.00138","repositories_listed":2,"syntology":null},{"url":"/paper/attention-is-not-all-you-need-for-commonsense","slug":"attention-is-not-all-you-need-for-commonsense","title":"Attention Is (not) All You Need for Commonsense Reasoning","date":"2019-05-31","arxiv_id":"1905.13497","repositories_listed":2,"syntology":null},{"url":"/paper/a-surprisingly-robust-trick-for-winograd","slug":"a-surprisingly-robust-trick-for-winograd","title":"A Surprisingly Robust Trick for Winograd Schema Challenge","date":"2019-05-15","arxiv_id":"1905.06290","repositories_listed":2,"syntology":null},{"url":"/paper/dual-supervised-learning-for-natural-language","slug":"dual-supervised-learning-for-natural-language","title":"Dual Supervised Learning for Natural Language Understanding and Generation","date":"2019-05-15","arxiv_id":"1905.06196","repositories_listed":2,"syntology":null},{"url":"/paper/bert-and-pals-projected-attention-layers-for","slug":"bert-and-pals-projected-attention-layers-for","title":"BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning","date":"2019-02-07","arxiv_id":"1902.02671","repositories_listed":2,"syntology":null},{"url":"/paper/chinese-lexical-analysis-with-deep-bi-gru-crf","slug":"chinese-lexical-analysis-with-deep-bi-gru-crf","title":"Chinese Lexical Analysis with Deep Bi-GRU-CRF Network","date":"2018-07-05","arxiv_id":"1807.01882","repositories_listed":2,"syntology":null},{"url":"/paper/jack-the-reader-a-machine-reading-framework","slug":"jack-the-reader-a-machine-reading-framework","title":"Jack the Reader - A Machine Reading Framework","date":"2018-06-20","arxiv_id":"1806.08727","repositories_listed":2,"syntology":null},{"url":"/paper/a-simple-method-for-commonsense-reasoning","slug":"a-simple-method-for-commonsense-reasoning","title":"A Simple Method for Commonsense Reasoning","date":"2018-06-07","arxiv_id":"1806.02847","repositories_listed":2,"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/a-simple-method-for-commonsense-reasoning#ran","syntology_url":"https://syntology.ai/paper/1806.02847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.02847"}},"official":null}},{"url":"/paper/sentences-with-gapping-parsing-and","slug":"sentences-with-gapping-parsing-and","title":"Sentences with Gapping: Parsing and Reconstructing Elided Predicates","date":"2018-04-18","arxiv_id":"1804.06922","repositories_listed":2,"syntology":null},{"url":"/paper/allennlp-a-deep-semantic-natural-language","slug":"allennlp-a-deep-semantic-natural-language","title":"AllenNLP: A Deep Semantic Natural Language Processing Platform","date":"2018-03-20","arxiv_id":"1803.07640","repositories_listed":2,"syntology":null},{"url":"/paper/evaluating-scoped-meaning-representations","slug":"evaluating-scoped-meaning-representations","title":"Evaluating Scoped Meaning Representations","date":"2018-02-23","arxiv_id":"1802.08599","repositories_listed":2,"syntology":null},{"url":"/paper/recurrent-neural-network-based-sentence","slug":"recurrent-neural-network-based-sentence","title":"Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference","date":"2017-08-04","arxiv_id":"1708.01353","repositories_listed":2,"syntology":null},{"url":"/paper/text-summarization-using-abstract-meaning","slug":"text-summarization-using-abstract-meaning","title":"Text Summarization using Abstract Meaning Representation","date":"2017-06-06","arxiv_id":"1706.01678","repositories_listed":2,"syntology":null},{"url":"/paper/learning-recurrent-span-representations-for","slug":"learning-recurrent-span-representations-for","title":"Learning Recurrent Span Representations for Extractive Question Answering","date":"2016-11-04","arxiv_id":"1611.01436","repositories_listed":2,"syntology":null},{"url":"/paper/wikireading-a-novel-large-scale-language","slug":"wikireading-a-novel-large-scale-language","title":"WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia","date":"2016-08-11","arxiv_id":"1608.03542","repositories_listed":2,"syntology":null},{"url":"/paper/a-probabilistic-generative-grammar-for","slug":"a-probabilistic-generative-grammar-for","title":"A Probabilistic Generative Grammar for Semantic Parsing","date":"2016-06-20","arxiv_id":"1606.06361","repositories_listed":2,"syntology":null},{"url":"/paper/vision-language-action-models-in-robotic","slug":"vision-language-action-models-in-robotic","title":"Vision Language Action Models in Robotic Manipulation: A Systematic Review","date":"2025-07-14","arxiv_id":"2507.10672","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-vision-language-action-models-for-1","slug":"a-survey-on-vision-language-action-models-for-1","title":"A Survey on Vision-Language-Action Models for Autonomous Driving","date":"2025-06-30","arxiv_id":"2506.24044","repositories_listed":1,"syntology":null},{"url":"/paper/dialect-normalization-using-large-language","slug":"dialect-normalization-using-large-language","title":"Dialect Normalization using Large Language Models and Morphological Rules","date":"2025-06-10","arxiv_id":"2506.08907","repositories_listed":1,"syntology":null},{"url":"/paper/edgeprofiler-a-fast-profiling-framework-for","slug":"edgeprofiler-a-fast-profiling-framework-for","title":"EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model","date":"2025-06-06","arxiv_id":"2506.09061","repositories_listed":1,"syntology":null},{"url":"/paper/can-compressed-llms-truly-act-an-empirical","slug":"can-compressed-llms-truly-act-an-empirical","title":"Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression","date":"2025-05-26","arxiv_id":"2505.19433","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/can-compressed-llms-truly-act-an-empirical#ran","syntology_url":"https://syntology.ai/paper/2505.19433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19433"}},"official":{"repos":["pprp/acbench"],"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","unlocated"]}}},{"url":"/paper/large-language-models-meet-knowledge-graphs-1","slug":"large-language-models-meet-knowledge-graphs-1","title":"Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities","date":"2025-05-26","arxiv_id":"2505.20099","repositories_listed":1,"syntology":null},{"url":"/paper/arctic-text2sql-r1-simple-rewards-strong","slug":"arctic-text2sql-r1-simple-rewards-strong","title":"Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL","date":"2025-05-22","arxiv_id":"2505.20315","repositories_listed":1,"syntology":null},{"url":"/paper/logic-of-thought-empowering-large-language","slug":"logic-of-thought-empowering-large-language","title":"Logic-of-Thought: Empowering Large Language Models with Logic Programs for Solving Puzzles in Natural Language","date":"2025-05-22","arxiv_id":"2505.16114","repositories_listed":1,"syntology":null},{"url":"/paper/transfer-of-structural-knowledge-from","slug":"transfer-of-structural-knowledge-from","title":"Transfer of Structural Knowledge from Synthetic Languages","date":"2025-05-21","arxiv_id":"2505.15769","repositories_listed":1,"syntology":null},{"url":"/paper/a-case-study-of-cross-lingual-zero-shot","slug":"a-case-study-of-cross-lingual-zero-shot","title":"A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs","date":"2025-05-19","arxiv_id":"2505.13173","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-representation-alignment","slug":"cross-lingual-representation-alignment","title":"Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning","date":"2025-05-19","arxiv_id":"2505.13628","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/cross-lingual-representation-alignment#ran","syntology_url":"https://syntology.ai/paper/2505.13628","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.13628"}},"official":{"repos":["nkrasner/cl-clip-align"],"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/do-llms-memorize-recommendation-datasets-a","slug":"do-llms-memorize-recommendation-datasets-a","title":"Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M","date":"2025-05-15","arxiv_id":"2505.10212","repositories_listed":1,"syntology":null},{"url":"/paper/a-social-robot-with-inner-speech-for-dietary","slug":"a-social-robot-with-inner-speech-for-dietary","title":"A Social Robot with Inner Speech for Dietary Guidance","date":"2025-05-13","arxiv_id":"2505.08664","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-of-qwen3-quantization","slug":"an-empirical-study-of-qwen3-quantization","title":"An Empirical Study of Qwen3 Quantization","date":"2025-05-04","arxiv_id":"2505.02214","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"7 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; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/an-empirical-study-of-qwen3-quantization#ran","syntology_url":"https://syntology.ai/paper/2505.02214","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.02214"}},"official":{"repos":["efficient-ml/qwen3-quantization"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/oet-optimization-based-prompt-injection","slug":"oet-optimization-based-prompt-injection","title":"OET: Optimization-based prompt injection Evaluation Toolkit","date":"2025-05-01","arxiv_id":"2505.00843","repositories_listed":1,"syntology":null},{"url":"/paper/auto-slurp-a-benchmark-dataset-for-evaluating","slug":"auto-slurp-a-benchmark-dataset-for-evaluating","title":"Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant","date":"2025-04-25","arxiv_id":"2504.18373","repositories_listed":1,"syntology":null},{"url":"/paper/td-suite-all-batteries-included-framework-for","slug":"td-suite-all-batteries-included-framework-for","title":"TD-Suite: All Batteries Included Framework for Technical Debt Classification","date":"2025-04-15","arxiv_id":"2504.11085","repositories_listed":1,"syntology":null},{"url":"/paper/can-you-map-it-to-english-the-role-of-cross","slug":"can-you-map-it-to-english-the-role-of-cross","title":"Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs","date":"2025-04-13","arxiv_id":"2504.09378","repositories_listed":1,"syntology":null},{"url":"/paper/lori-reducing-cross-task-interference-in","slug":"lori-reducing-cross-task-interference-in","title":"LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation","date":"2025-04-10","arxiv_id":"2504.07448","repositories_listed":1,"syntology":null},{"url":"/paper/aroma-autonomous-rank-one-matrix-adaptation","slug":"aroma-autonomous-rank-one-matrix-adaptation","title":"AROMA: Autonomous Rank-one Matrix Adaptation","date":"2025-04-06","arxiv_id":"2504.05343","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/aroma-autonomous-rank-one-matrix-adaptation#ran","syntology_url":"https://syntology.ai/paper/2504.05343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.05343"}},"official":{"repos":["shudun23/aroma"],"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/decoupling-angles-and-strength-in-low-rank","slug":"decoupling-angles-and-strength-in-low-rank","title":"DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation","date":"2025-03-23","arxiv_id":"2503.18225","repositories_listed":1,"syntology":null},{"url":"/paper/valid-text-to-sql-generation-with-unification","slug":"valid-text-to-sql-generation-with-unification","title":"Valid Text-to-SQL Generation with Unification-based DeepStochLog","date":"2025-03-17","arxiv_id":"2503.13342","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/valid-text-to-sql-generation-with-unification#ran","syntology_url":"https://syntology.ai/paper/2503.13342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13342"}},"official":{"repos":["ML-KULeuven/deepstochlog-lm"],"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/mrceval-a-comprehensive-challenging-and","slug":"mrceval-a-comprehensive-challenging-and","title":"MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark","date":"2025-03-10","arxiv_id":"2503.07144","repositories_listed":1,"syntology":null},{"url":"/paper/an-information-theoretic-multi-task","slug":"an-information-theoretic-multi-task","title":"An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding","date":"2025-03-06","arxiv_id":"2503.04667","repositories_listed":1,"syntology":null},{"url":"/paper/bevdriver-leveraging-bev-maps-in-llms-for","slug":"bevdriver-leveraging-bev-maps-in-llms-for","title":"BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving","date":"2025-03-05","arxiv_id":"2503.03074","repositories_listed":1,"syntology":null},{"url":"/paper/make-lora-great-again-boosting-lora-with","slug":"make-lora-great-again-boosting-lora-with","title":"Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment","date":"2025-02-24","arxiv_id":"2502.16894","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":3,"n_ran_checked":3,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/make-lora-great-again-boosting-lora-with#ran","syntology_url":"https://syntology.ai/paper/2502.16894","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.16894"}},"official":{"repos":["facico/goat-peft"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bridging-text-and-vision-a-multi-view-text","slug":"bridging-text-and-vision-a-multi-view-text","title":"Bridging Text and Vision: A Multi-View Text-Vision Registration Approach for Cross-Modal Place Recognition","date":"2025-02-20","arxiv_id":"2502.14195","repositories_listed":1,"syntology":null},{"url":"/paper/nlora-nystrom-initiated-low-rank-adaptation","slug":"nlora-nystrom-initiated-low-rank-adaptation","title":"NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models","date":"2025-02-20","arxiv_id":"2502.14482","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-and-tackling-label-errors-in","slug":"understanding-and-tackling-label-errors-in","title":"Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding","date":"2025-02-18","arxiv_id":"2502.13297","repositories_listed":1,"syntology":null},{"url":"/paper/prot2chat-protein-llm-with-early-fusion-of","slug":"prot2chat-protein-llm-with-early-fusion-of","title":"Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure","date":"2025-02-07","arxiv_id":"2502.06846","repositories_listed":1,"syntology":null},{"url":"/paper/transformers-boost-the-performance-of","slug":"transformers-boost-the-performance-of","title":"Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes","date":"2025-02-04","arxiv_id":"2502.02672","repositories_listed":1,"syntology":null},{"url":"/paper/joint-localization-and-activation-editing-for","slug":"joint-localization-and-activation-editing-for","title":"Joint Localization and Activation Editing for Low-Resource Fine-Tuning","date":"2025-02-03","arxiv_id":"2502.01179","repositories_listed":1,"syntology":null},{"url":"/paper/causal-graphs-meet-thoughts-enhancing-complex","slug":"causal-graphs-meet-thoughts-enhancing-complex","title":"Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in Graph-Augmented LLMs","date":"2025-01-24","arxiv_id":"2501.14892","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-de-composition-capabilities","slug":"investigating-the-de-composition-capabilities","title":"Investigating the (De)Composition Capabilities of Large Language Models in Natural-to-Formal Language Conversion","date":"2025-01-24","arxiv_id":"2501.14649","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"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 1 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/investigating-the-de-composition-capabilities#ran","syntology_url":"https://syntology.ai/paper/2501.14649","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.14649"}},"official":{"repos":["xzy-xzy/dedc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"aa601952cbc34c2953486c3f575de16cd60dbf9f2ce21c863c3fbb94165678e0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}