{"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/reranking/papers/2","list_of":"/task/reranking","task":"Reranking","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":6,"rows_per_page":100,"rows":[101,200],"of":586,"counts":{"archive_papers_tagged":586,"with_a_code_link":216,"where_syntology_ran_a_sample":59,"not_listed_spam_title":0,"listed":586,"listed_where_code_ran":59,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":45,"every_run_a_failure_of_syntologys_instrument":14,"listed_with_a_run_with_no_instrument_failure":45,"listed_every_run_a_failure_of_syntologys_instrument":14,"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/reranking","prev":"/task/reranking","next":"/task/reranking/papers/3","papers":[{"url":"/paper/synchronous-faithfulness-monitoring-for","slug":"synchronous-faithfulness-monitoring-for","title":"Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13692","repositories_listed":1,"syntology":null},{"url":"/paper/linkgpt-teaching-large-language-models-to","slug":"linkgpt-teaching-large-language-models-to","title":"LinkGPT: Teaching Large Language Models To Predict Missing Links","date":"2024-06-07","arxiv_id":"2406.04640","repositories_listed":1,"syntology":null},{"url":"/paper/passage-specific-prompt-tuning-for-passage","slug":"passage-specific-prompt-tuning-for-passage","title":"Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models","date":"2024-05-31","arxiv_id":"2405.20654","repositories_listed":1,"syntology":null},{"url":"/paper/rechorus2-0-a-modular-and-task-flexible","slug":"rechorus2-0-a-modular-and-task-flexible","title":"ReChorus2.0: A Modular and Task-Flexible Recommendation Library","date":"2024-05-28","arxiv_id":"2405.18058","repositories_listed":1,"syntology":null},{"url":"/paper/rag-rlrc-laysum-at-biolaysumm-integrating","slug":"rag-rlrc-laysum-at-biolaysumm-integrating","title":"RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts","date":"2024-05-21","arxiv_id":"2405.13179","repositories_listed":1,"syntology":null},{"url":"/paper/improving-technical-how-to-query-accuracy","slug":"improving-technical-how-to-query-accuracy","title":"Enhancing Mobile \"How-to\" Queries with Automated Search Results Verification and Reranking","date":"2024-04-13","arxiv_id":"2404.08860","repositories_listed":1,"syntology":null},{"url":"/paper/rar-b-reasoning-as-retrieval-benchmark","slug":"rar-b-reasoning-as-retrieval-benchmark","title":"RAR-b: Reasoning as Retrieval Benchmark","date":"2024-04-09","arxiv_id":"2404.06347","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rar-b-reasoning-as-retrieval-benchmark#ran","syntology_url":"https://syntology.ai/paper/2404.06347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06347"}},"official":{"repos":["gowitheflow-1998/rar-b"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/aragog-advanced-rag-output-grading","slug":"aragog-advanced-rag-output-grading","title":"ARAGOG: Advanced RAG Output Grading","date":"2024-04-01","arxiv_id":"2404.01037","repositories_listed":1,"syntology":null},{"url":"/paper/twolar-a-two-step-llm-augmented-distillation","slug":"twolar-a-two-step-llm-augmented-distillation","title":"TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking","date":"2024-03-26","arxiv_id":"2403.17759","repositories_listed":1,"syntology":null},{"url":"/paper/instupr-instruction-based-unsupervised","slug":"instupr-instruction-based-unsupervised","title":"InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models","date":"2024-03-25","arxiv_id":"2403.16435","repositories_listed":1,"syntology":null},{"url":"/paper/easy-to-hard-generalization-scalable","slug":"easy-to-hard-generalization-scalable","title":"Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision","date":"2024-03-14","arxiv_id":"2403.09472","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/easy-to-hard-generalization-scalable#ran","syntology_url":"https://syntology.ai/paper/2403.09472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09472"}},"official":{"repos":["edward-sun/easy-to-hard"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/listt5-listwise-reranking-with-fusion-in","slug":"listt5-listwise-reranking-with-fusion-in","title":"ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval","date":"2024-02-24","arxiv_id":"2402.15838","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"3 ran (of which 1 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) · 2 unverified","sample_list":"/paper/listt5-listwise-reranking-with-fusion-in#ran","syntology_url":"https://syntology.ai/paper/2402.15838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15838"}},"official":{"repos":["soyoung97/listt5"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/ecorank-budget-constrained-text-re-ranking","slug":"ecorank-budget-constrained-text-re-ranking","title":"EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models","date":"2024-02-16","arxiv_id":"2402.10866","repositories_listed":1,"syntology":null},{"url":"/paper/list-aware-reranking-truncation-joint-model","slug":"list-aware-reranking-truncation-joint-model","title":"List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation","date":"2024-02-05","arxiv_id":"2402.02764","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/list-aware-reranking-truncation-joint-model#ran","syntology_url":"https://syntology.ai/paper/2402.02764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02764"}},"official":{"repos":["xsc1234/genrt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mining-fine-grained-image-text-alignment-for","slug":"mining-fine-grained-image-text-alignment-for","title":"Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training","date":"2024-01-04","arxiv_id":"2401.02347","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":8,"phrase":"3 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/mining-fine-grained-image-text-alignment-for#ran","syntology_url":"https://syntology.ai/paper/2401.02347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.02347"}},"official":{"repos":["artanic30/maccap"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/helping-or-herding-reward-model-ensembles","slug":"helping-or-herding-reward-model-ensembles","title":"Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking","date":"2023-12-14","arxiv_id":"2312.09244","repositories_listed":1,"syntology":null},{"url":"/paper/affective-and-dynamic-beam-search-for-story","slug":"affective-and-dynamic-beam-search-for-story","title":"Affective and Dynamic Beam Search for Story Generation","date":"2023-10-23","arxiv_id":"2310.15079","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/affective-and-dynamic-beam-search-for-story#ran","syntology_url":"https://syntology.ai/paper/2310.15079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15079"}},"official":{"repos":["tenghaohuang/affgen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-empirical-study-of-translation-hypothesis","slug":"an-empirical-study-of-translation-hypothesis","title":"An Empirical Study of Translation Hypothesis Ensembling with Large Language Models","date":"2023-10-17","arxiv_id":"2310.11430","repositories_listed":1,"syntology":null},{"url":"/paper/found-in-the-middle-permutation-self","slug":"found-in-the-middle-permutation-self","title":"Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models","date":"2023-10-11","arxiv_id":"2310.07712","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/found-in-the-middle-permutation-self#ran","syntology_url":"https://syntology.ai/paper/2310.07712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07712"}},"official":{"repos":["castorini/perm-sc"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hypr-a-comprehensive-study-for-asr-hypothesis","slug":"hypr-a-comprehensive-study-for-asr-hypothesis","title":"HypR: A comprehensive study for ASR hypothesis revising with a reference corpus","date":"2023-09-18","arxiv_id":"2309.09838","repositories_listed":1,"syntology":null},{"url":"/paper/learning-evaluation-models-from-large","slug":"learning-evaluation-models-from-large","title":"Learning Evaluation Models from Large Language Models for Sequence Generation","date":"2023-08-08","arxiv_id":"2308.04386","repositories_listed":1,"syntology":null},{"url":"/paper/how-about-kind-of-generating-hedges-using-end","slug":"how-about-kind-of-generating-hedges-using-end","title":"How About Kind of Generating Hedges using End-to-End Neural Models?","date":"2023-06-26","arxiv_id":"2306.14696","repositories_listed":1,"syntology":null},{"url":"/paper/t5-sr-a-unified-seq-to-seq-decoding-strategy","slug":"t5-sr-a-unified-seq-to-seq-decoding-strategy","title":"T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing","date":"2023-06-14","arxiv_id":"2306.08368","repositories_listed":1,"syntology":null},{"url":"/paper/generative-flow-network-for-listwise","slug":"generative-flow-network-for-listwise","title":"Generative Flow Network for Listwise Recommendation","date":"2023-06-04","arxiv_id":"2306.02239","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generative-flow-network-for-listwise#ran","syntology_url":"https://syntology.ai/paper/2306.02239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02239"}},"official":{"repos":["charliemat/gfn4rec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/eel-efficiently-encoding-lattices-for","slug":"eel-efficiently-encoding-lattices-for","title":"EEL: Efficiently Encoding Lattices for Reranking","date":"2023-06-01","arxiv_id":"2306.00947","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/eel-efficiently-encoding-lattices-for#ran","syntology_url":"https://syntology.ai/paper/2306.00947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00947"}},"official":{"repos":["prasanns/eel-reranking"],"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/towards-argument-aware-abstractive","slug":"towards-argument-aware-abstractive","title":"Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking","date":"2023-06-01","arxiv_id":"2306.00672","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-the-ranking-context-of-dense","slug":"enhancing-the-ranking-context-of-dense","title":"Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors","date":"2023-05-25","arxiv_id":"2305.15720","repositories_listed":1,"syntology":null},{"url":"/paper/bidirectional-transformer-reranker-for","slug":"bidirectional-transformer-reranker-for","title":"Bidirectional Transformer Reranker for Grammatical Error Correction","date":"2023-05-22","arxiv_id":"2305.13000","repositories_listed":1,"syntology":null},{"url":"/paper/improving-toponym-resolution-with-better","slug":"improving-toponym-resolution-with-better","title":"Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution","date":"2023-05-18","arxiv_id":"2305.11315","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-and-collaborative-passage-reranking","slug":"hybrid-and-collaborative-passage-reranking","title":"Hybrid and Collaborative Passage Reranking","date":"2023-05-16","arxiv_id":"2305.09313","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-zero-shot-cross-lingual-retrieval-by","slug":"boosting-zero-shot-cross-lingual-retrieval-by","title":"Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data","date":"2023-05-09","arxiv_id":"2305.05295","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-writer-retrieval-using-netrvlad","slug":"unsupervised-writer-retrieval-using-netrvlad","title":"Towards Writer Retrieval for Historical Datasets","date":"2023-05-09","arxiv_id":"2305.05358","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-selection-of-anchor-items-for-cur","slug":"adaptive-selection-of-anchor-items-for-cur","title":"Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition","date":"2023-05-04","arxiv_id":"2305.02996","repositories_listed":1,"syntology":null},{"url":"/paper/stir-siamese-transformer-for-image-retrieval","slug":"stir-siamese-transformer-for-image-retrieval","title":"STIR: Siamese Transformer for Image Retrieval Postprocessing","date":"2023-04-26","arxiv_id":"2304.13393","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-specialization-for-knowledge-based","slug":"semantic-specialization-for-knowledge-based","title":"Semantic Specialization for Knowledge-based Word Sense Disambiguation","date":"2023-04-22","arxiv_id":"2304.11340","repositories_listed":1,"syntology":null},{"url":"/paper/elastic-weight-removal-for-faithful-and","slug":"elastic-weight-removal-for-faithful-and","title":"Elastic Weight Removal for Faithful and Abstractive Dialogue Generation","date":"2023-03-30","arxiv_id":"2303.17574","repositories_listed":1,"syntology":null},{"url":"/paper/irgen-generative-modeling-for-image-retrieval","slug":"irgen-generative-modeling-for-image-retrieval","title":"IRGen: Generative Modeling for Image Retrieval","date":"2023-03-17","arxiv_id":"2303.10126","repositories_listed":1,"syntology":null},{"url":"/paper/udapdr-unsupervised-domain-adaptation-via-llm","slug":"udapdr-unsupervised-domain-adaptation-via-llm","title":"UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers","date":"2023-03-01","arxiv_id":"2303.00807","repositories_listed":1,"syntology":null},{"url":"/paper/lever-learning-to-verify-language-to-code","slug":"lever-learning-to-verify-language-to-code","title":"LEVER: Learning to Verify Language-to-Code Generation with Execution","date":"2023-02-16","arxiv_id":"2302.08468","repositories_listed":1,"syntology":{"n":22,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":1,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/lever-learning-to-verify-language-to-code#ran","syntology_url":"https://syntology.ai/paper/2302.08468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08468"}},"official":{"repos":["niansong1996/lever"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/pier-permutation-level-interest-based-end-to","slug":"pier-permutation-level-interest-based-end-to","title":"PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce","date":"2023-02-06","arxiv_id":"2302.03487","repositories_listed":1,"syntology":null},{"url":"/paper/r2former-unified-retrieval-and-reranking","slug":"r2former-unified-retrieval-and-reranking","title":"R2Former: Unified Retrieval and Reranking Transformer for Place Recognition","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/visconde-multi-document-qa-with-gpt-3-and","slug":"visconde-multi-document-qa-with-gpt-3-and","title":"Visconde: Multi-document QA with GPT-3 and Neural Reranking","date":"2022-12-19","arxiv_id":"2212.09656","repositories_listed":1,"syntology":null},{"url":"/paper/nir-prompt-a-multi-task-generalized-neural","slug":"nir-prompt-a-multi-task-generalized-neural","title":"NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework","date":"2022-12-01","arxiv_id":"2212.00229","repositories_listed":1,"syntology":null},{"url":"/paper/coder-reviewer-reranking-for-code-generation","slug":"coder-reviewer-reranking-for-code-generation","title":"Coder Reviewer Reranking for Code Generation","date":"2022-11-29","arxiv_id":"2211.16490","repositories_listed":1,"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/coder-reviewer-reranking-for-code-generation#ran","syntology_url":"https://syntology.ai/paper/2211.16490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.16490"}},"official":{"repos":["facebookresearch/coder_reviewer_reranking"],"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/dyrren-a-dynamic-retriever-reranker-generator","slug":"dyrren-a-dynamic-retriever-reranker-generator","title":"DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data","date":"2022-11-23","arxiv_id":"2211.12668","repositories_listed":1,"syntology":null},{"url":"/paper/a-bird-s-eye-view-of-reranking-from-list","slug":"a-bird-s-eye-view-of-reranking-from-list","title":"A Bird's-eye View of Reranking: from List Level to Page Level","date":"2022-11-17","arxiv_id":"2211.09303","repositories_listed":1,"syntology":null},{"url":"/paper/chinese-spelling-check-with-nearest-neighbors","slug":"chinese-spelling-check-with-nearest-neighbors","title":"Error-Robust Retrieval for Chinese Spelling Check","date":"2022-11-15","arxiv_id":"2211.07843","repositories_listed":1,"syntology":null},{"url":"/paper/reranking-overgenerated-responses-for-end-to","slug":"reranking-overgenerated-responses-for-end-to","title":"Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems","date":"2022-11-07","arxiv_id":"2211.03648","repositories_listed":1,"syntology":null},{"url":"/paper/improving-bilingual-lexicon-induction-with-1","slug":"improving-bilingual-lexicon-induction-with-1","title":"Improving Bilingual Lexicon Induction with Cross-Encoder Reranking","date":"2022-10-30","arxiv_id":"2210.16953","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-bilingual-lexicon-induction-with-1#ran","syntology_url":"https://syntology.ai/paper/2210.16953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16953"}},"official":{"repos":["cambridgeltl/BLICEr"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-nearest-neighbor-search-for-cross","slug":"efficient-nearest-neighbor-search-for-cross","title":"Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization","date":"2022-10-23","arxiv_id":"2210.12579","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/efficient-nearest-neighbor-search-for-cross#ran","syntology_url":"https://syntology.ai/paper/2210.12579","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12579"}},"official":{"repos":["iesl/anncur"],"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/sentence-representation-learning-with","slug":"sentence-representation-learning-with","title":"Sentence Representation Learning with Generative Objective rather than Contrastive Objective","date":"2022-10-16","arxiv_id":"2210.08474","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/sentence-representation-learning-with#ran","syntology_url":"https://syntology.ai/paper/2210.08474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.08474"}},"official":{"repos":["chengzhipanpan/paser"],"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/re3-generating-longer-stories-with-recursive","slug":"re3-generating-longer-stories-with-recursive","title":"Re3: Generating Longer Stories With Recursive Reprompting and Revision","date":"2022-10-13","arxiv_id":"2210.06774","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/re3-generating-longer-stories-with-recursive#ran","syntology_url":"https://syntology.ai/paper/2210.06774","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06774"}},"official":{"repos":["yangkevin2/emnlp22-re3-story-generation"],"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/coditt5-pretraining-for-source-code-and","slug":"coditt5-pretraining-for-source-code-and","title":"CoditT5: Pretraining for Source Code and Natural Language Editing","date":"2022-08-10","arxiv_id":"2208.05446","repositories_listed":1,"syntology":null},{"url":"/paper/re2g-retrieve-rerank-generate-2","slug":"re2g-retrieve-rerank-generate-2","title":"Re2G: Retrieve, Rerank, Generate","date":"2022-07-13","arxiv_id":"2207.06300","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/re2g-retrieve-rerank-generate-2#ran","syntology_url":"https://syntology.ai/paper/2207.06300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06300"}},"official":{"repos":["ibm/kgi-slot-filling"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-abstractive-dialogue-2","slug":"unsupervised-abstractive-dialogue-2","title":"Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion","date":"2022-05-26","arxiv_id":"2205.13108","repositories_listed":1,"syntology":null},{"url":"/paper/transcormer-transformer-for-sentence-scoring","slug":"transcormer-transformer-for-sentence-scoring","title":"Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling","date":"2022-05-25","arxiv_id":"2205.12986","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/transcormer-transformer-for-sentence-scoring#ran","syntology_url":"https://syntology.ai/paper/2205.12986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.12986"}},"official":null}},{"url":"/paper/hlatr-enhance-multi-stage-text-retrieval-with","slug":"hlatr-enhance-multi-stage-text-retrieval-with","title":"HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking","date":"2022-05-21","arxiv_id":"2205.10569","repositories_listed":1,"syntology":null},{"url":"/paper/certified-error-control-of-candidate-set","slug":"certified-error-control-of-candidate-set","title":"Certified Error Control of Candidate Set Pruning for Two-Stage Relevance Ranking","date":"2022-05-19","arxiv_id":"2205.09638","repositories_listed":1,"syntology":null},{"url":"/paper/twist-decoding-diverse-generators-guide-each","slug":"twist-decoding-diverse-generators-guide-each","title":"Twist Decoding: Diverse Generators Guide Each Other","date":"2022-05-19","arxiv_id":"2205.09273","repositories_listed":1,"syntology":null},{"url":"/paper/quality-aware-decoding-for-neural-machine-1","slug":"quality-aware-decoding-for-neural-machine-1","title":"Quality-Aware Decoding for Neural Machine Translation","date":"2022-05-02","arxiv_id":"2205.00978","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/quality-aware-decoding-for-neural-machine-1#ran","syntology_url":"https://syntology.ai/paper/2205.00978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00978"}},"official":{"repos":["deep-spin/qaware-decode"],"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/generating-scientific-definitions-with-1","slug":"generating-scientific-definitions-with-1","title":"Generating Scientific Definitions with Controllable Complexity","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-interaction-reranking-with-user","slug":"multi-level-interaction-reranking-with-user","title":"Multi-Level Interaction Reranking with User Behavior History","date":"2022-04-20","arxiv_id":"2204.09370","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-level-interaction-reranking-with-user#ran","syntology_url":"https://syntology.ai/paper/2204.09370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09370"}},"official":{"repos":["YunjiaXi/Multi-Level-Interaction-Reranking"],"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/unsupervised-cross-task-generalization-via","slug":"unsupervised-cross-task-generalization-via","title":"Unsupervised Cross-Task Generalization via Retrieval Augmentation","date":"2022-04-17","arxiv_id":"2204.07937","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":1,"n_ran_checked":11,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 1 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unsupervised-cross-task-generalization-via#ran","syntology_url":"https://syntology.ai/paper/2204.07937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07937"}},"official":{"repos":["INK-USC/ReCross"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":1,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/parameter-efficient-neural-reranking-for","slug":"parameter-efficient-neural-reranking-for","title":"Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval","date":"2022-04-05","arxiv_id":"2204.02292","repositories_listed":1,"syntology":null},{"url":"/paper/article-reranking-by-memory-enhanced-key-1","slug":"article-reranking-by-memory-enhanced-key-1","title":"Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims","date":"2021-12-20","arxiv_id":"2112.10322","repositories_listed":1,"syntology":null},{"url":"/paper/coder-an-efficient-framework-for-improving","slug":"coder-an-efficient-framework-for-improving","title":"CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking","date":"2021-12-16","arxiv_id":"2112.08766","repositories_listed":1,"syntology":null},{"url":"/paper/local-citation-recommendation-with","slug":"local-citation-recommendation-with","title":"Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking","date":"2021-12-02","arxiv_id":"2112.01206","repositories_listed":1,"syntology":null},{"url":"/paper/facebook-ais-wmt21-news-translation-task","slug":"facebook-ais-wmt21-news-translation-task","title":"Facebook AI’s WMT21 News Translation Task Submission","date":"2021-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/text2mol-cross-modal-molecule-retrieval-with","slug":"text2mol-cross-modal-molecule-retrieval-with","title":"Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries","date":"2021-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/2nd-place-solution-to-google-landmark-1","slug":"2nd-place-solution-to-google-landmark-1","title":"2nd Place Solution to Google Landmark Retrieval 2021","date":"2021-10-08","arxiv_id":"2110.04294","repositories_listed":1,"syntology":null},{"url":"/paper/mmarco-a-multilingual-version-of-ms-marco","slug":"mmarco-a-multilingual-version-of-ms-marco","title":"mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset","date":"2021-08-31","arxiv_id":"2108.13897","repositories_listed":1,"syntology":null},{"url":"/paper/reader-guided-passage-reranking-for-open-1","slug":"reader-guided-passage-reranking-for-open-1","title":"Reader-Guided Passage Reranking for Open-Domain Question Answering","date":"2021-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/modeling-diagnostic-label-correlation-for-1","slug":"modeling-diagnostic-label-correlation-for-1","title":"Modeling Diagnostic Label Correlation for Automatic ICD Coding","date":"2021-06-24","arxiv_id":"2106.12800","repositories_listed":1,"syntology":null},{"url":"/paper/assessing-the-use-of-prosody-in-constituency","slug":"assessing-the-use-of-prosody-in-constituency","title":"Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts","date":"2021-06-14","arxiv_id":"2106.07794","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-passage-retrieval-with-hashing-for","slug":"efficient-passage-retrieval-with-hashing-for","title":"Efficient Passage Retrieval with Hashing for Open-domain Question Answering","date":"2021-06-02","arxiv_id":"2106.00882","repositories_listed":1,"syntology":null},{"url":"/paper/neural-quality-estimation-with-multiple","slug":"neural-quality-estimation-with-multiple","title":"Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction","date":"2021-05-10","arxiv_id":"2105.04443","repositories_listed":1,"syntology":null},{"url":"/paper/not-all-relevance-scores-are-equal-efficient","slug":"not-all-relevance-scores-are-equal-efficient","title":"Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models","date":"2021-05-10","arxiv_id":"2105.04651","repositories_listed":1,"syntology":null},{"url":"/paper/first-the-worst-finding-better-gender","slug":"first-the-worst-finding-better-gender","title":"First the worst: Finding better gender translations during beam search","date":"2021-04-15","arxiv_id":"2104.07429","repositories_listed":1,"syntology":null},{"url":"/paper/refsum-refactoring-neural-summarization","slug":"refsum-refactoring-neural-summarization","title":"RefSum: Refactoring Neural Summarization","date":"2021-04-15","arxiv_id":"2104.07210","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/refsum-refactoring-neural-summarization#ran","syntology_url":"https://syntology.ai/paper/2104.07210","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07210"}},"official":{"repos":["yixinL7/Refactoring-Summarization"],"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/fine-grained-fashion-similarity-prediction-by","slug":"fine-grained-fashion-similarity-prediction-by","title":"Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning","date":"2021-04-06","arxiv_id":"2104.02429","repositories_listed":1,"syntology":null},{"url":"/paper/instance-level-image-retrieval-using","slug":"instance-level-image-retrieval-using","title":"Instance-level Image Retrieval using Reranking Transformers","date":"2021-03-22","arxiv_id":"2103.12236","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 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; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/instance-level-image-retrieval-using#ran","syntology_url":"https://syntology.ai/paper/2103.12236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12236"}},"official":{"repos":["uvavision/rerankingtransformer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/incremental-beam-manipulation-for-natural","slug":"incremental-beam-manipulation-for-natural","title":"Incremental Beam Manipulation for Natural Language Generation","date":"2021-02-04","arxiv_id":"2102.02574","repositories_listed":1,"syntology":null},{"url":"/paper/reader-guided-passage-reranking-for-open","slug":"reader-guided-passage-reranking-for-open","title":"Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering","date":"2021-01-01","arxiv_id":"2101.00294","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":3,"n_honours":2,"n_violates":1,"n_no_contract":2,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/reader-guided-passage-reranking-for-open#ran","syntology_url":"https://syntology.ai/paper/2101.00294","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.00294"}},"official":{"repos":["morningmoni/GAR"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/literature-retrieval-for-precision-medicine","slug":"literature-retrieval-for-precision-medicine","title":"Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization","date":"2020-12-17","arxiv_id":"2012.09355","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-large-pretrained-models-for-webnlg","slug":"leveraging-large-pretrained-models-for-webnlg","title":"Leveraging Large Pretrained Models for WebNLG 2020","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-nlg-for-methodius-from-rst-meaning","slug":"neural-nlg-for-methodius-from-rst-meaning","title":"Neural NLG for Methodius: From RST Meaning Representations to Texts","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/retrieve-rerank-read-then-iterate-answering","slug":"retrieve-rerank-read-then-iterate-answering","title":"Answering Open-Domain Questions of Varying Reasoning Steps from Text","date":"2020-10-23","arxiv_id":"2010.12527","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/retrieve-rerank-read-then-iterate-answering#ran","syntology_url":"https://syntology.ai/paper/2010.12527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12527"}},"official":{"repos":["beerqa/irrr"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/pretrained-transformers-for-text-ranking-bert","slug":"pretrained-transformers-for-text-ranking-bert","title":"Pretrained Transformers for Text Ranking: BERT and Beyond","date":"2020-10-13","arxiv_id":"2010.06467","repositories_listed":1,"syntology":null},{"url":"/paper/energy-based-reranking-improving-neural","slug":"energy-based-reranking-improving-neural","title":"Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models","date":"2020-09-20","arxiv_id":"2009.13267","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/energy-based-reranking-improving-neural#ran","syntology_url":"https://syntology.ai/paper/2009.13267","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.13267"}},"official":{"repos":["rooshenas/ebr_mt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/parade-passage-representation-aggregation-for","slug":"parade-passage-representation-aggregation-for","title":"PARADE: Passage Representation Aggregation for Document Reranking","date":"2020-08-20","arxiv_id":"2008.09093","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/parade-passage-representation-aggregation-for#ran","syntology_url":"https://syntology.ai/paper/2008.09093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.09093"}},"official":{"repos":["canjiali/PARADE"],"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/but-fit-at-semeval-2020-task-4-multilingual","slug":"but-fit-at-semeval-2020-task-4-multilingual","title":"BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense","date":"2020-08-17","arxiv_id":"2008.07259","repositories_listed":1,"syntology":null},{"url":"/paper/conformer-kernel-with-query-term-independence","slug":"conformer-kernel-with-query-term-independence","title":"Conformer-Kernel with Query Term Independence for Document Retrieval","date":"2020-07-20","arxiv_id":"2007.10434","repositories_listed":1,"syntology":null},{"url":"/paper/deep-retrieval-an-end-to-end-learnable","slug":"deep-retrieval-an-end-to-end-learnable","title":"Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations","date":"2020-07-12","arxiv_id":"2007.07203","repositories_listed":1,"syntology":null},{"url":"/paper/wikiumls-aligning-umls-to-wikipedia-via-cross","slug":"wikiumls-aligning-umls-to-wikipedia-via-cross","title":"WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking","date":"2020-05-04","arxiv_id":"2005.01281","repositories_listed":1,"syntology":null},{"url":"/paper/tired-of-topic-models-clusters-of-pretrained","slug":"tired-of-topic-models-clusters-of-pretrained","title":"Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!","date":"2020-04-30","arxiv_id":"2004.14914","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/tired-of-topic-models-clusters-of-pretrained#ran","syntology_url":"https://syntology.ai/paper/2004.14914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14914"}},"official":{"repos":["adalmia96/Cluster-Analysis"],"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/fast-and-accurate-deep-bidirectional-language","slug":"fast-and-accurate-deep-bidirectional-language","title":"Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning","date":"2020-04-17","arxiv_id":"2004.08097","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/fast-and-accurate-deep-bidirectional-language#ran","syntology_url":"https://syntology.ai/paper/2004.08097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.08097"}},"official":{"repos":["joongbo/tta"],"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/have-your-text-and-use-it-too-end-to-end","slug":"have-your-text-and-use-it-too-end-to-end","title":"Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity","date":"2020-04-08","arxiv_id":"2004.06577","repositories_listed":1,"syntology":null},{"url":"/paper/trec-cast-2019-the-conversational-assistance","slug":"trec-cast-2019-the-conversational-assistance","title":"TREC CAsT 2019: The Conversational Assistance Track Overview","date":"2020-03-30","arxiv_id":"2003.13624","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-fashion-similarity-learning-by","slug":"fine-grained-fashion-similarity-learning-by","title":"Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network","date":"2020-02-07","arxiv_id":"2002.02814","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/fine-grained-fashion-similarity-learning-by#ran","syntology_url":"https://syntology.ai/paper/2002.02814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.02814"}},"official":{"repos":["Maryeon/asen"],"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"]}}},{"url":"/paper/cost-effective-interactive-attention-learning-1","slug":"cost-effective-interactive-attention-learning-1","title":"Cost-effective Interactive Attention Learning with Neural Attention Process","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null}],"record_sha256":"91e931a881024546ea2ae874873dddca15f654541a87239d9dd1e9607e8004a9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}