{"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/retrieval/papers/24","list_of":"/task/retrieval","task":"Retrieval","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":24,"pages_in_order":143,"rows_per_page":100,"rows":[2301,2400],"of":14297,"counts":{"archive_papers_tagged":14297,"with_a_code_link":5274,"where_syntology_ran_a_sample":1303,"not_listed_spam_title":0,"listed":14297,"listed_where_code_ran":1303,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1067,"every_run_a_failure_of_syntologys_instrument":236,"listed_with_a_run_with_no_instrument_failure":1067,"listed_every_run_a_failure_of_syntologys_instrument":236,"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/retrieval","prev":"/task/retrieval/papers/23","next":"/task/retrieval/papers/25","papers":[{"url":"/paper/language-only-efficient-training-of-zero-shot","slug":"language-only-efficient-training-of-zero-shot","title":"Language-only Efficient Training of Zero-shot Composed Image Retrieval","date":"2023-12-04","arxiv_id":"2312.01998","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":8,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/language-only-efficient-training-of-zero-shot#ran","syntology_url":"https://syntology.ai/paper/2312.01998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.01998"}},"official":{"repos":["navervision/lincir"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/context-retrieval-via-normalized-contextual","slug":"context-retrieval-via-normalized-contextual","title":"Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent","date":"2023-12-01","arxiv_id":"2312.00774","repositories_listed":1,"syntology":null},{"url":"/paper/removing-biases-from-molecular","slug":"removing-biases-from-molecular","title":"Removing Biases from Molecular Representations via Information Maximization","date":"2023-12-01","arxiv_id":"2312.00718","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":9,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/removing-biases-from-molecular#ran","syntology_url":"https://syntology.ai/paper/2312.00718","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.00718"}},"official":{"repos":["uhlerlab/infocore"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-two-tower-matching-learning-sparse","slug":"beyond-two-tower-matching-learning-sparse","title":"Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation","date":"2023-11-30","arxiv_id":"2311.18213","repositories_listed":1,"syntology":null},{"url":"/paper/hkust-at-semeval-2023-task-1-visual-word","slug":"hkust-at-semeval-2023-task-1-visual-word","title":"HKUST at SemEval-2023 Task 1: Visual Word Sense Disambiguation with Context Augmentation and Visual Assistance","date":"2023-11-30","arxiv_id":"2311.18273","repositories_listed":1,"syntology":null},{"url":"/paper/hubness-reduction-improves-sentence-bert","slug":"hubness-reduction-improves-sentence-bert","title":"Hubness Reduction Improves Sentence-BERT Semantic Spaces","date":"2023-11-30","arxiv_id":"2311.18364","repositories_listed":1,"syntology":null},{"url":"/paper/label-efficient-training-of-small-task","slug":"label-efficient-training-of-small-task","title":"Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models","date":"2023-11-30","arxiv_id":"2311.18237","repositories_listed":1,"syntology":null},{"url":"/paper/mllms-augmented-visual-language","slug":"mllms-augmented-visual-language","title":"MLLMs-Augmented Visual-Language Representation Learning","date":"2023-11-30","arxiv_id":"2311.18765","repositories_listed":1,"syntology":null},{"url":"/paper/sketch-input-method-editor-a-comprehensive","slug":"sketch-input-method-editor-a-comprehensive","title":"Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition","date":"2023-11-30","arxiv_id":"2311.18254","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-pursuit-prompting-for-zero-shot","slug":"knowledge-pursuit-prompting-for-zero-shot","title":"Contextual Knowledge Pursuit for Faithful Visual Synthesis","date":"2023-11-29","arxiv_id":"2311.17898","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-a-unified-video","slug":"bridging-the-gap-a-unified-video","title":"Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection","date":"2023-11-28","arxiv_id":"2311.16464","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/bridging-the-gap-a-unified-video#ran","syntology_url":"https://syntology.ai/paper/2311.16464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16464"}},"official":{"repos":["easonxiao-888/uvcom"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/climatex-do-llms-accurately-assess-human","slug":"climatex-do-llms-accurately-assess-human","title":"ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?","date":"2023-11-28","arxiv_id":"2311.17107","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/climatex-do-llms-accurately-assess-human#ran","syntology_url":"https://syntology.ai/paper/2311.17107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17107"}},"official":{"repos":["rlacombe/climatex"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rankinggpt-empowering-large-language-models","slug":"rankinggpt-empowering-large-language-models","title":"A Two-Stage Adaptation of Large Language Models for Text Ranking","date":"2023-11-28","arxiv_id":"2311.16720","repositories_listed":1,"syntology":null},{"url":"/paper/boot-and-switch-alternating-distillation-for","slug":"boot-and-switch-alternating-distillation-for","title":"Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval","date":"2023-11-27","arxiv_id":"2311.15564","repositories_listed":1,"syntology":null},{"url":"/paper/noisy-self-training-with-synthetic-queries","slug":"noisy-self-training-with-synthetic-queries","title":"Noisy Self-Training with Synthetic Queries for Dense Retrieval","date":"2023-11-27","arxiv_id":"2311.15563","repositories_listed":1,"syntology":null},{"url":"/paper/removing-nsfw-concepts-from-vision-and","slug":"removing-nsfw-concepts-from-vision-and","title":"Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models","date":"2023-11-27","arxiv_id":"2311.16254","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"4 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/removing-nsfw-concepts-from-vision-and#ran","syntology_url":"https://syntology.ai/paper/2311.16254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.16254"}},"official":{"repos":["aimagelab/safe-clip"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/sut-a-new-multi-purpose-synthetic-dataset-for","slug":"sut-a-new-multi-purpose-synthetic-dataset-for","title":"SUT: a new multi-purpose synthetic dataset for Farsi document image analysis","date":"2023-11-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-for-person-zero-shot-composed-person","slug":"word-for-person-zero-shot-composed-person","title":"Automatic Synthetic Data and Fine-grained Adaptive Feature Alignment for Composed Person Retrieval","date":"2023-11-25","arxiv_id":"2311.16515","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-robustness-of-text-image","slug":"benchmarking-robustness-of-text-image","title":"Benchmarking Robustness of Text-Image Composed Retrieval","date":"2023-11-24","arxiv_id":"2311.14837","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"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) · 2 unverified","sample_list":"/paper/benchmarking-robustness-of-text-image#ran","syntology_url":"https://syntology.ai/paper/2311.14837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14837"}},"official":{"repos":["suntongtongtong/benchmark-robustness-text-image-compose-retrieval"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-mir-a-benchmark-and-empirical-study-on-3d","slug":"3d-mir-a-benchmark-and-empirical-study-on-3d","title":"3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology","date":"2023-11-23","arxiv_id":"2311.13752","repositories_listed":1,"syntology":null},{"url":"/paper/ai-generated-images-introduce-invisible","slug":"ai-generated-images-introduce-invisible","title":"Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images","date":"2023-11-23","arxiv_id":"2311.14084","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-tree-of-thought-reasoning-for","slug":"probabilistic-tree-of-thought-reasoning-for","title":"Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions","date":"2023-11-23","arxiv_id":"2311.13982","repositories_listed":1,"syntology":null},{"url":"/paper/query-by-activity-video-in-the-wild","slug":"query-by-activity-video-in-the-wild","title":"Query by Activity Video in the Wild","date":"2023-11-23","arxiv_id":"2311.13895","repositories_listed":1,"syntology":null},{"url":"/paper/autokg-efficient-automated-knowledge-graph","slug":"autokg-efficient-automated-knowledge-graph","title":"AutoKG: Efficient Automated Knowledge Graph Generation for Language Models","date":"2023-11-22","arxiv_id":"2311.14740","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/autokg-efficient-automated-knowledge-graph#ran","syntology_url":"https://syntology.ai/paper/2311.14740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.14740"}},"official":{"repos":["wispcarey/autokg"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-augmented-layout-transformer-for","slug":"retrieval-augmented-layout-transformer-for","title":"Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation","date":"2023-11-22","arxiv_id":"2311.13602","repositories_listed":1,"syntology":null},{"url":"/paper/attribute-aware-deep-hashing-with-self","slug":"attribute-aware-deep-hashing-with-self","title":"Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval","date":"2023-11-21","arxiv_id":"2311.12894","repositories_listed":1,"syntology":null},{"url":"/paper/csmed-bridging-the-dataset-gap-in-automated","slug":"csmed-bridging-the-dataset-gap-in-automated","title":"CSMeD: Bridging the Dataset Gap in Automated Citation Screening for Systematic Literature Reviews","date":"2023-11-21","arxiv_id":"2311.12474","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/csmed-bridging-the-dataset-gap-in-automated#ran","syntology_url":"https://syntology.ai/paper/2311.12474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.12474"}},"official":{"repos":["wojciechkusa/systematic-review-datasets"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-robust-text-retrieval-with","slug":"towards-robust-text-retrieval-with","title":"Towards Robust Text Retrieval with Progressive Learning","date":"2023-11-20","arxiv_id":"2311.11691","repositories_listed":1,"syntology":null},{"url":"/paper/shapemaker-self-supervised-joint-shape","slug":"shapemaker-self-supervised-joint-shape","title":"ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation","date":"2023-11-18","arxiv_id":"2311.11106","repositories_listed":1,"syntology":null},{"url":"/paper/ares-an-automated-evaluation-framework-for","slug":"ares-an-automated-evaluation-framework-for","title":"ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems","date":"2023-11-16","arxiv_id":"2311.09476","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ares-an-automated-evaluation-framework-for#ran","syntology_url":"https://syntology.ai/paper/2311.09476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09476"}},"official":{"repos":["stanford-futuredata/ares"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/back-to-basics-a-simple-recipe-for-improving","slug":"back-to-basics-a-simple-recipe-for-improving","title":"Back to Basics: A Simple Recipe for Improving Out-of-Domain Retrieval in Dense Encoders","date":"2023-11-16","arxiv_id":"2311.09765","repositories_listed":1,"syntology":null},{"url":"/paper/itercqr-iterative-conversational-query","slug":"itercqr-iterative-conversational-query","title":"IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance","date":"2023-11-16","arxiv_id":"2311.09820","repositories_listed":1,"syntology":null},{"url":"/paper/knowledgemath-knowledge-intensive-math-word","slug":"knowledgemath-knowledge-intensive-math-word","title":"FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains","date":"2023-11-16","arxiv_id":"2311.09797","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/knowledgemath-knowledge-intensive-math-word#ran","syntology_url":"https://syntology.ai/paper/2311.09797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09797"}},"official":{"repos":["yale-nlp/knowledgemath"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/the-song-describer-dataset-a-corpus-of-audio","slug":"the-song-describer-dataset-a-corpus-of-audio","title":"The Song Describer Dataset: a Corpus of Audio Captions for Music-and-Language Evaluation","date":"2023-11-16","arxiv_id":"2311.10057","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-song-describer-dataset-a-corpus-of-audio#ran","syntology_url":"https://syntology.ai/paper/2311.10057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.10057"}},"official":{"repos":["mulab-mir/song-describer-dataset"],"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/combining-transfer-learning-with-in-context","slug":"combining-transfer-learning-with-in-context","title":"Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning","date":"2023-11-15","arxiv_id":"2311.08894","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-evaluation-of-uncertainty","slug":"empirical-evaluation-of-uncertainty","title":"Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science","date":"2023-11-15","arxiv_id":"2311.09358","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/empirical-evaluation-of-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2311.09358","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09358"}},"official":{"repos":["pnnl/expert2"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ever-mitigating-hallucination-in-large","slug":"ever-mitigating-hallucination-in-large","title":"Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification","date":"2023-11-15","arxiv_id":"2311.09114","repositories_listed":1,"syntology":null},{"url":"/paper/lepard-a-large-scale-dataset-of-judges-citing","slug":"lepard-a-large-scale-dataset-of-judges-citing","title":"LePaRD: A Large-Scale Dataset of Judges Citing Precedents","date":"2023-11-15","arxiv_id":"2311.09356","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"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) · 2 unverified","sample_list":"/paper/lepard-a-large-scale-dataset-of-judges-citing#ran","syntology_url":"https://syntology.ai/paper/2311.09356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09356"}},"official":{"repos":["rmahari/lepard"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/videocon-robust-video-language-alignment-via","slug":"videocon-robust-video-language-alignment-via","title":"VideoCon: Robust Video-Language Alignment via Contrast Captions","date":"2023-11-15","arxiv_id":"2311.10111","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/videocon-robust-video-language-alignment-via#ran","syntology_url":"https://syntology.ai/paper/2311.10111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.10111"}},"official":{"repos":["hritikbansal/videocon"],"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":["official"]}}},{"url":"/paper/acid-abstractive-content-based-ids-for","slug":"acid-abstractive-content-based-ids-for","title":"Summarization-Based Document IDs for Generative Retrieval with Language Models","date":"2023-11-14","arxiv_id":"2311.08593","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":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) · 0 unverified","sample_list":"/paper/acid-abstractive-content-based-ids-for#ran","syntology_url":"https://syntology.ai/paper/2311.08593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08593"}},"official":{"repos":["lihaoxin2020/summarization-based-document-ids-for-generative-retrieval"],"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/choralsynth-synthetic-dataset-of-choral","slug":"choralsynth-synthetic-dataset-of-choral","title":"ChoralSynth: Synthetic Dataset of Choral Singing","date":"2023-11-14","arxiv_id":"2311.08350","repositories_listed":1,"syntology":null},{"url":"/paper/improving-hateful-memes-detection-via","slug":"improving-hateful-memes-detection-via","title":"Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning","date":"2023-11-14","arxiv_id":"2311.08110","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/improving-hateful-memes-detection-via#ran","syntology_url":"https://syntology.ai/paper/2311.08110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08110"}},"official":{"repos":["JingbiaoMei/RGCL"],"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/llatrieval-llm-verified-retrieval-for","slug":"llatrieval-llm-verified-retrieval-for","title":"LLatrieval: LLM-Verified Retrieval for Verifiable Generation","date":"2023-11-14","arxiv_id":"2311.07838","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":11,"phrase":"8 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/llatrieval-llm-verified-retrieval-for#ran","syntology_url":"https://syntology.ai/paper/2311.07838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07838"}},"official":{"repos":["beastyz/llm-verified-retrieval"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rest-retrieval-based-speculative-decoding","slug":"rest-retrieval-based-speculative-decoding","title":"REST: Retrieval-Based Speculative Decoding","date":"2023-11-14","arxiv_id":"2311.08252","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":1,"n_no_contract":5,"n_pointer_only":4,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rest-retrieval-based-speculative-decoding#ran","syntology_url":"https://syntology.ai/paper/2311.08252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.08252"}},"official":{"repos":["fasterdecoding/rest"],"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/text-retrieval-with-multi-stage-re-ranking","slug":"text-retrieval-with-multi-stage-re-ranking","title":"Text Retrieval with Multi-Stage Re-Ranking Models","date":"2023-11-14","arxiv_id":"2311.07994","repositories_listed":1,"syntology":null},{"url":"/paper/pretrain-like-you-inference-masked-tuning","slug":"pretrain-like-you-inference-masked-tuning","title":"Pretrain like Your Inference: Masked Tuning Improves Zero-Shot Composed Image Retrieval","date":"2023-11-13","arxiv_id":"2311.07622","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pretrain-like-you-inference-masked-tuning#ran","syntology_url":"https://syntology.ai/paper/2311.07622","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07622"}},"official":{"repos":["Chen-Junyang-cn/PLI"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/minimum-description-length-hopfield-networks","slug":"minimum-description-length-hopfield-networks","title":"Minimum Description Length Hopfield Networks","date":"2023-11-11","arxiv_id":"2311.06518","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-hallucination-in-large-language","slug":"a-survey-on-hallucination-in-large-language","title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","date":"2023-11-09","arxiv_id":"2311.05232","repositories_listed":1,"syntology":null},{"url":"/paper/llava-plus-learning-to-use-tools-for-creating","slug":"llava-plus-learning-to-use-tools-for-creating","title":"LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents","date":"2023-11-09","arxiv_id":"2311.05437","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":1,"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/llava-plus-learning-to-use-tools-for-creating#ran","syntology_url":"https://syntology.ai/paper/2311.05437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05437"}},"official":{"repos":["LLaVA-VL/LLaVA-Plus-Codebase"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/rapid-training-free-retrieval-based-log","slug":"rapid-training-free-retrieval-based-log","title":"RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information","date":"2023-11-09","arxiv_id":"2311.05160","repositories_listed":1,"syntology":null},{"url":"/paper/text-representation-distillation-via","slug":"text-representation-distillation-via","title":"Text Representation Distillation via Information Bottleneck Principle","date":"2023-11-09","arxiv_id":"2311.05472","repositories_listed":1,"syntology":null},{"url":"/paper/training-clip-models-on-data-from-scientific","slug":"training-clip-models-on-data-from-scientific","title":"Training CLIP models on Data from Scientific Papers","date":"2023-11-08","arxiv_id":"2311.04711","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-cross-model-learning-in","slug":"weakly-supervised-cross-model-learning-in","title":"Weakly supervised cross-modal learning in high-content screening","date":"2023-11-08","arxiv_id":"2311.04678","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":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/weakly-supervised-cross-model-learning-in#ran","syntology_url":"https://syntology.ai/paper/2311.04678","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.04678"}},"official":{"repos":["gwatkinson/jump_download"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/deeppatent2-a-large-scale-benchmarking-corpus","slug":"deeppatent2-a-large-scale-benchmarking-corpus","title":"DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding","date":"2023-11-07","arxiv_id":"2311.04098","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-model-based-long-tail-query","slug":"large-language-model-based-long-tail-query","title":"Large Language Model based Long-tail Query Rewriting in Taobao Search","date":"2023-11-07","arxiv_id":"2311.03758","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/large-language-model-based-long-tail-query#ran","syntology_url":"https://syntology.ai/paper/2311.03758","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03758"}},"official":null}},{"url":"/paper/proceedings-of-the-5th-international-workshop-3","slug":"proceedings-of-the-5th-international-workshop-3","title":"Proceedings of the 5th International Workshop on Reading Music Systems","date":"2023-11-07","arxiv_id":"2311.04091","repositories_listed":1,"syntology":null},{"url":"/paper/a-foundation-model-for-music-informatics","slug":"a-foundation-model-for-music-informatics","title":"A Foundation Model for Music Informatics","date":"2023-11-06","arxiv_id":"2311.03318","repositories_listed":1,"syntology":null},{"url":"/paper/glen-generative-retrieval-via-lexical-index","slug":"glen-generative-retrieval-via-lexical-index","title":"GLEN: Generative Retrieval via Lexical Index Learning","date":"2023-11-06","arxiv_id":"2311.03057","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/glen-generative-retrieval-via-lexical-index#ran","syntology_url":"https://syntology.ai/paper/2311.03057","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03057"}},"official":{"repos":["skleee/GLEN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/ovir-3d-open-vocabulary-3d-instance-retrieval","slug":"ovir-3d-open-vocabulary-3d-instance-retrieval","title":"OVIR-3D: Open-Vocabulary 3D Instance Retrieval Without Training on 3D Data","date":"2023-11-06","arxiv_id":"2311.02873","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/ovir-3d-open-vocabulary-3d-instance-retrieval#ran","syntology_url":"https://syntology.ai/paper/2311.02873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.02873"}},"official":{"repos":["shiyoung77/ovir-3d"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/chata-towards-an-intelligent-question-answer","slug":"chata-towards-an-intelligent-question-answer","title":"AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs","date":"2023-11-05","arxiv_id":"2311.02775","repositories_listed":1,"syntology":null},{"url":"/paper/mixcon3d-synergizing-multi-view-and-cross","slug":"mixcon3d-synergizing-multi-view-and-cross","title":"Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training","date":"2023-11-03","arxiv_id":"2311.01734","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":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/mixcon3d-synergizing-multi-view-and-cross#ran","syntology_url":"https://syntology.ai/paper/2311.01734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.01734"}},"official":{"repos":["ucsc-vlaa/mixcon3d"],"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/multi-eup-the-multilingual-european","slug":"multi-eup-the-multilingual-european","title":"Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval","date":"2023-11-03","arxiv_id":"2311.01870","repositories_listed":1,"syntology":null},{"url":"/paper/crush4sql-collective-retrieval-using-schema","slug":"crush4sql-collective-retrieval-using-schema","title":"CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL","date":"2023-11-02","arxiv_id":"2311.01173","repositories_listed":1,"syntology":null},{"url":"/paper/metarevision-meta-learning-with-retrieval-for","slug":"metarevision-meta-learning-with-retrieval-for","title":"MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition","date":"2023-11-02","arxiv_id":"2311.01580","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-scaling-retrieval-augmentation","slug":"the-effect-of-scaling-retrieval-augmentation","title":"The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models","date":"2023-11-02","arxiv_id":"2311.01307","repositories_listed":1,"syntology":null},{"url":"/paper/caseformer-pre-training-for-legal-case","slug":"caseformer-pre-training-for-legal-case","title":"Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions","date":"2023-11-01","arxiv_id":"2311.00333","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-based-reconstruction-for-time","slug":"retrieval-based-reconstruction-for-time","title":"REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning","date":"2023-11-01","arxiv_id":"2311.00519","repositories_listed":1,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"10 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; 2 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/retrieval-based-reconstruction-for-time#ran","syntology_url":"https://syntology.ai/paper/2311.00519","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00519"}},"official":{"repos":["maxxu05/rebar"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/general-purpose-retrieval-enhanced-medical","slug":"general-purpose-retrieval-enhanced-medical","title":"General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History","date":"2023-10-31","arxiv_id":"2310.20204","repositories_listed":1,"syntology":null},{"url":"/paper/object-centric-video-representation-for-long","slug":"object-centric-video-representation-for-long","title":"Object-centric Video Representation for Long-term Action Anticipation","date":"2023-10-31","arxiv_id":"2311.00180","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/object-centric-video-representation-for-long#ran","syntology_url":"https://syntology.ai/paper/2311.00180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.00180"}},"official":{"repos":["brown-palm/ObjectPrompt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/birdsat-cross-view-contrastive-masked","slug":"birdsat-cross-view-contrastive-masked","title":"BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping","date":"2023-10-29","arxiv_id":"2310.19168","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/birdsat-cross-view-contrastive-masked#ran","syntology_url":"https://syntology.ai/paper/2310.19168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19168"}},"official":{"repos":["mvrl/birdsat"],"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/mill-mutual-verification-with-large-language","slug":"mill-mutual-verification-with-large-language","title":"MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion","date":"2023-10-29","arxiv_id":"2310.19056","repositories_listed":1,"syntology":null},{"url":"/paper/poisoning-retrieval-corpora-by-injecting","slug":"poisoning-retrieval-corpora-by-injecting","title":"Poisoning Retrieval Corpora by Injecting Adversarial Passages","date":"2023-10-29","arxiv_id":"2310.19156","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/poisoning-retrieval-corpora-by-injecting#ran","syntology_url":"https://syntology.ai/paper/2310.19156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19156"}},"official":{"repos":["princeton-nlp/corpus-poisoning"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/testa-temporal-spatial-token-aggregation-for","slug":"testa-temporal-spatial-token-aggregation-for","title":"TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding","date":"2023-10-29","arxiv_id":"2310.19060","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/testa-temporal-spatial-token-aggregation-for#ran","syntology_url":"https://syntology.ai/paper/2310.19060","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19060"}},"official":{"repos":["renshuhuai-andy/testa"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/dense-retrieval-as-indirect-supervision-for","slug":"dense-retrieval-as-indirect-supervision-for","title":"Dense Retrieval as Indirect Supervision for Large-space Decision Making","date":"2023-10-28","arxiv_id":"2310.18619","repositories_listed":1,"syntology":null},{"url":"/paper/a-prior-instruction-representation-framework","slug":"a-prior-instruction-representation-framework","title":"A Prior Instruction Representation Framework for Remote Sensing Image-text Retrieval","date":"2023-10-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/detrimental-contexts-in-open-domain-question","slug":"detrimental-contexts-in-open-domain-question","title":"Detrimental Contexts in Open-Domain Question Answering","date":"2023-10-27","arxiv_id":"2310.18077","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-corpus-error-in-question-answering","slug":"knowledge-corpus-error-in-question-answering","title":"Knowledge Corpus Error in Question Answering","date":"2023-10-27","arxiv_id":"2310.18076","repositories_listed":1,"syntology":null},{"url":"/paper/lipsim-a-provably-robust-perceptual","slug":"lipsim-a-provably-robust-perceptual","title":"LipSim: A Provably Robust Perceptual Similarity Metric","date":"2023-10-27","arxiv_id":"2310.18274","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"6 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lipsim-a-provably-robust-perceptual#ran","syntology_url":"https://syntology.ai/paper/2310.18274","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18274"}},"official":{"repos":["saraghazanfari/lipsim"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/petailor-improving-large-language-model-by","slug":"petailor-improving-large-language-model-by","title":"Benchingmaking Large Langage Models in Biomedical Triple Extraction","date":"2023-10-27","arxiv_id":"2310.18463","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-bias-and-fairness-in-gender","slug":"evaluating-bias-and-fairness-in-gender","title":"Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Models","date":"2023-10-26","arxiv_id":"2310.17530","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/evaluating-bias-and-fairness-in-gender#ran","syntology_url":"https://syntology.ai/paper/2310.17530","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17530"}},"official":{"repos":["coastalcph/gender-neutral-vl"],"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/accomontage-3-full-band-accompaniment","slug":"accomontage-3-full-band-accompaniment","title":"Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling","date":"2023-10-25","arxiv_id":"2310.16334","repositories_listed":1,"syntology":{"n":16,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/accomontage-3-full-band-accompaniment#ran","syntology_url":"https://syntology.ai/paper/2310.16334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16334"}},"official":{"repos":["zhaojw1998/accomontage-3"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/attention-lens-a-tool-for-mechanistically","slug":"attention-lens-a-tool-for-mechanistically","title":"Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism","date":"2023-10-25","arxiv_id":"2310.16270","repositories_listed":1,"syntology":null},{"url":"/paper/clinfo-ai-an-open-source-retrieval-augmented","slug":"clinfo-ai-an-open-source-retrieval-augmented","title":"Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature","date":"2023-10-24","arxiv_id":"2310.16146","repositories_listed":1,"syntology":null},{"url":"/paper/creating-a-silver-standard-for-patent","slug":"creating-a-silver-standard-for-patent","title":"Creating a silver standard for patent simplification","date":"2023-10-24","arxiv_id":"2310.15689","repositories_listed":1,"syntology":null},{"url":"/paper/kitab-evaluating-llms-on-constraint","slug":"kitab-evaluating-llms-on-constraint","title":"KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval","date":"2023-10-24","arxiv_id":"2310.15511","repositories_listed":1,"syntology":null},{"url":"/paper/length-is-a-curse-and-a-blessing-for-document","slug":"length-is-a-curse-and-a-blessing-for-document","title":"Length is a Curse and a Blessing for Document-level Semantics","date":"2023-10-24","arxiv_id":"2310.16193","repositories_listed":1,"syntology":null},{"url":"/paper/muser-a-multi-view-similar-case-retrieval","slug":"muser-a-multi-view-similar-case-retrieval","title":"MUSER: A Multi-View Similar Case Retrieval Dataset","date":"2023-10-24","arxiv_id":"2310.15602","repositories_listed":1,"syntology":null},{"url":"/paper/tic-clip-continual-training-of-clip-models","slug":"tic-clip-continual-training-of-clip-models","title":"TiC-CLIP: Continual Training of CLIP Models","date":"2023-10-24","arxiv_id":"2310.16226","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":9,"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) · 4 unverified","sample_list":"/paper/tic-clip-continual-training-of-clip-models#ran","syntology_url":"https://syntology.ai/paper/2310.16226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16226"}},"official":{"repos":["apple/ml-tic-clip"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/bioimage-io-chatbot-a-personalized-assistant","slug":"bioimage-io-chatbot-a-personalized-assistant","title":"BioImage.IO Chatbot: A Community-Driven AI Assistant for Integrative Computational Bioimaging","date":"2023-10-23","arxiv_id":"2310.18351","repositories_listed":1,"syntology":null},{"url":"/paper/disc-finllm-a-chinese-financial-large","slug":"disc-finllm-a-chinese-financial-large","title":"DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning","date":"2023-10-23","arxiv_id":"2310.15205","repositories_listed":1,"syntology":null},{"url":"/paper/diversify-question-generation-with-retrieval","slug":"diversify-question-generation-with-retrieval","title":"Diversify Question Generation with Retrieval-Augmented Style Transfer","date":"2023-10-23","arxiv_id":"2310.14503","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"9 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diversify-question-generation-with-retrieval#ran","syntology_url":"https://syntology.ai/paper/2310.14503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14503"}},"official":{"repos":["gouqi666/rast"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-searching-and-grounding-multi","slug":"joint-searching-and-grounding-multi","title":"Joint Searching and Grounding: Multi-Granularity Video Content Retrieval","date":"2023-10-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-adversarial-training-for-1","slug":"semantic-aware-adversarial-training-for-1","title":"Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval","date":"2023-10-23","arxiv_id":"2310.14637","repositories_listed":1,"syntology":null},{"url":"/paper/tree-of-clarifications-answering-ambiguous","slug":"tree-of-clarifications-answering-ambiguous","title":"Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models","date":"2023-10-23","arxiv_id":"2310.14696","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/tree-of-clarifications-answering-ambiguous#ran","syntology_url":"https://syntology.ai/paper/2310.14696","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14696"}},"official":{"repos":["gankim/tree-of-clarifications"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/why-should-i-review-this-paper-unifying","slug":"why-should-i-review-this-paper-unifying","title":"Chain-of-Factors Paper-Reviewer Matching","date":"2023-10-23","arxiv_id":"2310.14483","repositories_listed":1,"syntology":null},{"url":"/paper/merging-generated-and-retrieved-knowledge-for","slug":"merging-generated-and-retrieved-knowledge-for","title":"Merging Generated and Retrieved Knowledge for Open-Domain QA","date":"2023-10-22","arxiv_id":"2310.14393","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/merging-generated-and-retrieved-knowledge-for#ran","syntology_url":"https://syntology.ai/paper/2310.14393","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14393"}},"official":{"repos":["yunx-z/combo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/neretrieve-dataset-for-next-generation-named","slug":"neretrieve-dataset-for-next-generation-named","title":"NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval","date":"2023-10-22","arxiv_id":"2310.14282","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-and-multimodal","slug":"large-language-models-and-multimodal","title":"Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation","date":"2023-10-21","arxiv_id":"2310.14025","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":1,"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/large-language-models-and-multimodal#ran","syntology_url":"https://syntology.ai/paper/2310.14025","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14025"}},"official":{"repos":["anastasiakrith/multimodal-retrieval-for-vwsd"],"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/unlock-multi-modal-capability-of-dense","slug":"unlock-multi-modal-capability-of-dense","title":"MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin","date":"2023-10-21","arxiv_id":"2310.14037","repositories_listed":1,"syntology":{"n":18,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unlock-multi-modal-capability-of-dense#ran","syntology_url":"https://syntology.ai/paper/2310.14037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14037"}},"official":{"repos":["openmatch/marvel"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-quality-based-syntactic-template-retriever","slug":"a-quality-based-syntactic-template-retriever","title":"A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation","date":"2023-10-20","arxiv_id":"2310.13262","repositories_listed":1,"syntology":null}],"record_sha256":"e1a2a15f0278cfa5269d6c946bb90ebd40412f40cae01ce42896fb3b2527ccd3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}