{"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/29","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":29,"pages_in_order":143,"rows_per_page":100,"rows":[2801,2900],"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/28","next":"/task/retrieval/papers/30","papers":[{"url":"/paper/how-to-prompt-llms-for-text-to-sql-a-study-in","slug":"how-to-prompt-llms-for-text-to-sql-a-study-in","title":"How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings","date":"2023-05-19","arxiv_id":"2305.11853","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":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) · 1 unverified","sample_list":"/paper/how-to-prompt-llms-for-text-to-sql-a-study-in#ran","syntology_url":"https://syntology.ai/paper/2305.11853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11853"}},"official":{"repos":["shuaichenchang/prompt-text-to-sql"],"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/inference-time-re-ranker-relevance-feedback","slug":"inference-time-re-ranker-relevance-feedback","title":"ReFIT: Relevance Feedback from a Reranker during Inference","date":"2023-05-19","arxiv_id":"2305.11744","repositories_listed":1,"syntology":null},{"url":"/paper/learning-sequence-descriptor-based-on","slug":"learning-sequence-descriptor-based-on","title":"Learning Sequence Descriptor based on Spatio-Temporal Attention for Visual Place Recognition","date":"2023-05-19","arxiv_id":"2305.11467","repositories_listed":1,"syntology":null},{"url":"/paper/quest-a-retrieval-dataset-of-entity-seeking","slug":"quest-a-retrieval-dataset-of-entity-seeking","title":"QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations","date":"2023-05-19","arxiv_id":"2305.11694","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/malm-mask-augmentation-based-local-matching","slug":"malm-mask-augmentation-based-local-matching","title":"MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval","date":"2023-05-18","arxiv_id":"2305.11327","repositories_listed":1,"syntology":null},{"url":"/paper/query-performance-prediction-from-ad-hoc-to","slug":"query-performance-prediction-from-ad-hoc-to","title":"Query Performance Prediction: From Ad-hoc to Conversational Search","date":"2023-05-18","arxiv_id":"2305.10923","repositories_listed":1,"syntology":null},{"url":"/paper/regen-zero-shot-text-classification-via","slug":"regen-zero-shot-text-classification-via","title":"ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval","date":"2023-05-18","arxiv_id":"2305.10703","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/regen-zero-shot-text-classification-via#ran","syntology_url":"https://syntology.ai/paper/2305.10703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10703"}},"official":{"repos":["yueyu1030/ReGen"],"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/seq-hgnn-learning-sequential-node","slug":"seq-hgnn-learning-sequential-node","title":"Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph","date":"2023-05-18","arxiv_id":"2305.10771","repositories_listed":1,"syntology":null},{"url":"/paper/the-web-can-be-your-oyster-for-improving","slug":"the-web-can-be-your-oyster-for-improving","title":"The Web Can Be Your Oyster for Improving Large Language Models","date":"2023-05-18","arxiv_id":"2305.10998","repositories_listed":1,"syntology":null},{"url":"/paper/tram-a-token-level-retrieval-augmented","slug":"tram-a-token-level-retrieval-augmented","title":"Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization","date":"2023-05-18","arxiv_id":"2305.11074","repositories_listed":1,"syntology":null},{"url":"/paper/from-region-to-patch-attribute-aware","slug":"from-region-to-patch-attribute-aware","title":"From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval","date":"2023-05-17","arxiv_id":"2305.10260","repositories_listed":1,"syntology":null},{"url":"/paper/multi-grained-knowledge-retrieval-for-end-to","slug":"multi-grained-knowledge-retrieval-for-end-to","title":"Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog","date":"2023-05-17","arxiv_id":"2305.10149","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/large-language-models-are-built-in","slug":"large-language-models-are-built-in","title":"Large Language Models are Built-in Autoregressive Search Engines","date":"2023-05-16","arxiv_id":"2305.09612","repositories_listed":1,"syntology":null},{"url":"/paper/phase-retrieval-via-model-free-power-flow","slug":"phase-retrieval-via-model-free-power-flow","title":"Phase Retrieval via Model-Free Power Flow Jacobian Recovery","date":"2023-05-16","arxiv_id":"2305.09661","repositories_listed":1,"syntology":null},{"url":"/paper/a-reproducible-extraction-of-training-images","slug":"a-reproducible-extraction-of-training-images","title":"A Reproducible Extraction of Training Images from Diffusion Models","date":"2023-05-15","arxiv_id":"2305.08694","repositories_listed":1,"syntology":null},{"url":"/paper/kepr-knowledge-enhancement-and-plausibility","slug":"kepr-knowledge-enhancement-and-plausibility","title":"KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering","date":"2023-05-15","arxiv_id":"2305.08347","repositories_listed":1,"syntology":null},{"url":"/paper/rl4f-generating-natural-language-feedback","slug":"rl4f-generating-natural-language-feedback","title":"RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs","date":"2023-05-15","arxiv_id":"2305.08844","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":6,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/rl4f-generating-natural-language-feedback#ran","syntology_url":"https://syntology.ai/paper/2305.08844","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08844"}},"official":{"repos":["feyzaakyurek/rl4f"],"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":["found_in_text","official"]}}},{"url":"/paper/multilingual-previously-fact-checked-claim","slug":"multilingual-previously-fact-checked-claim","title":"Multilingual Previously Fact-Checked Claim Retrieval","date":"2023-05-13","arxiv_id":"2305.07991","repositories_listed":1,"syntology":null},{"url":"/paper/nevir-negation-in-neural-information","slug":"nevir-negation-in-neural-information","title":"NevIR: Negation in Neural Information Retrieval","date":"2023-05-12","arxiv_id":"2305.07614","repositories_listed":1,"syntology":null},{"url":"/paper/a-general-purpose-multilingual-document","slug":"a-general-purpose-multilingual-document","title":"A General-Purpose Multilingual Document Encoder","date":"2023-05-11","arxiv_id":"2305.07016","repositories_listed":1,"syntology":null},{"url":"/paper/afriqa-cross-lingual-open-retrieval-question","slug":"afriqa-cross-lingual-open-retrieval-question","title":"AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages","date":"2023-05-11","arxiv_id":"2305.06897","repositories_listed":1,"syntology":null},{"url":"/paper/webcpm-interactive-web-search-for-chinese","slug":"webcpm-interactive-web-search-for-chinese","title":"WebCPM: Interactive Web Search for Chinese Long-form Question Answering","date":"2023-05-11","arxiv_id":"2305.06849","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/webcpm-interactive-web-search-for-chinese#ran","syntology_url":"https://syntology.ai/paper/2305.06849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06849"}},"official":{"repos":["thunlp/webcpm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/autonomous-gis-the-next-generation-ai-powered","slug":"autonomous-gis-the-next-generation-ai-powered","title":"Autonomous GIS: the next-generation AI-powered GIS","date":"2023-05-10","arxiv_id":"2305.06453","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-dense-retrieval-training-with","slug":"unsupervised-dense-retrieval-training-with","title":"Unsupervised Dense Retrieval Training with Web Anchors","date":"2023-05-10","arxiv_id":"2305.05834","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-visual-language-models-by-exploiting","slug":"boosting-visual-language-models-by-exploiting","title":"Boosting Visual-Language Models by Exploiting Hard Samples","date":"2023-05-09","arxiv_id":"2305.05208","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/the-role-of-relevance-in-fair-ranking","slug":"the-role-of-relevance-in-fair-ranking","title":"The Role of Relevance in Fair Ranking","date":"2023-05-09","arxiv_id":"2305.05608","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/the-role-of-relevance-in-fair-ranking#ran","syntology_url":"https://syntology.ai/paper/2305.05608","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05608"}},"official":{"repos":["aparna-b/fairrankingrelevance"],"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/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/vcsum-a-versatile-chinese-meeting","slug":"vcsum-a-versatile-chinese-meeting","title":"VCSUM: A Versatile Chinese Meeting Summarization Dataset","date":"2023-05-09","arxiv_id":"2305.05280","repositories_listed":1,"syntology":null},{"url":"/paper/elastichash-semantic-image-similarity-search","slug":"elastichash-semantic-image-similarity-search","title":"ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch","date":"2023-05-08","arxiv_id":"2305.04710","repositories_listed":1,"syntology":null},{"url":"/paper/joint-moment-retrieval-and-highlight","slug":"joint-moment-retrieval-and-highlight","title":"Joint Moment Retrieval and Highlight Detection Via Natural Language Queries","date":"2023-05-08","arxiv_id":"2305.04961","repositories_listed":1,"syntology":null},{"url":"/paper/less-is-more-removing-text-regions-improves","slug":"less-is-more-removing-text-regions-improves","title":"Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness","date":"2023-05-08","arxiv_id":"2305.05095","repositories_listed":1,"syntology":null},{"url":"/paper/recommender-systems-with-generative-retrieval","slug":"recommender-systems-with-generative-retrieval","title":"Recommender Systems with Generative Retrieval","date":"2023-05-08","arxiv_id":"2305.05065","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/recommender-systems-with-generative-retrieval#ran","syntology_url":"https://syntology.ai/paper/2305.05065","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05065"}},"official":null}},{"url":"/paper/searching-mobile-app-screens-via-text-doodle","slug":"searching-mobile-app-screens-via-text-doodle","title":"Searching Mobile App Screens via Text + Doodle","date":"2023-05-08","arxiv_id":"2305.06165","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modal-retrieval-for-motion-and-text-via","slug":"cross-modal-retrieval-for-motion-and-text-via","title":"Cross-Modal Retrieval for Motion and Text via DopTriple Loss","date":"2023-05-07","arxiv_id":"2305.04195","repositories_listed":1,"syntology":null},{"url":"/paper/miread-simple-method-for-learning-high","slug":"miread-simple-method-for-learning-high","title":"MIReAD: Simple Method for Learning High-quality Representations from Scientific Documents","date":"2023-05-07","arxiv_id":"2305.04177","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 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) · 1 unverified","sample_list":"/paper/miread-simple-method-for-learning-high#ran","syntology_url":"https://syntology.ai/paper/2305.04177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04177"}},"official":{"repos":["arazd/miread"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/openvivqa-task-dataset-and-multimodal-fusion","slug":"openvivqa-task-dataset-and-multimodal-fusion","title":"OpenViVQA: Task, Dataset, and Multimodal Fusion Models for Visual Question Answering in Vietnamese","date":"2023-05-07","arxiv_id":"2305.04183","repositories_listed":1,"syntology":null},{"url":"/paper/unified-demonstration-retriever-for-in","slug":"unified-demonstration-retriever-for-in","title":"Unified Demonstration Retriever for In-Context Learning","date":"2023-05-07","arxiv_id":"2305.04320","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unified-demonstration-retriever-for-in#ran","syntology_url":"https://syntology.ai/paper/2305.04320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04320"}},"official":{"repos":["kailv69/udr"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/augmenting-passage-representations-with-query","slug":"augmenting-passage-representations-with-query","title":"Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval","date":"2023-05-06","arxiv_id":"2305.03950","repositories_listed":1,"syntology":null},{"url":"/paper/fairness-in-image-search-a-study-of","slug":"fairness-in-image-search-a-study-of","title":"Fairness in Image Search: A Study of Occupational Stereotyping in Image Retrieval and its Debiasing","date":"2023-05-06","arxiv_id":"2305.03881","repositories_listed":1,"syntology":null},{"url":"/paper/srtk-a-toolkit-for-semantic-relevant-subgraph","slug":"srtk-a-toolkit-for-semantic-relevant-subgraph","title":"SRTK: A Toolkit for Semantic-relevant Subgraph Retrieval","date":"2023-05-06","arxiv_id":"2305.04101","repositories_listed":1,"syntology":null},{"url":"/paper/a-large-cross-modal-video-retrieval-dataset","slug":"a-large-cross-modal-video-retrieval-dataset","title":"A Large Cross-Modal Video Retrieval Dataset with Reading Comprehension","date":"2023-05-05","arxiv_id":"2305.03347","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":3,"phrase":"12 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-large-cross-modal-video-retrieval-dataset#ran","syntology_url":"https://syntology.ai/paper/2305.03347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03347"}},"official":{"repos":["callsys/textvr"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cola-a-benchmark-for-compositional-text-to-1","slug":"cola-a-benchmark-for-compositional-text-to-1","title":"COLA: A Benchmark for Compositional Text-to-image Retrieval","date":"2023-05-05","arxiv_id":"2305.03689","repositories_listed":1,"syntology":null},{"url":"/paper/damo-nlp-at-semeval-2023-task-2-a-unified","slug":"damo-nlp-at-semeval-2023-task-2-a-unified","title":"DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition","date":"2023-05-05","arxiv_id":"2305.03688","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/damo-nlp-at-semeval-2023-task-2-a-unified#ran","syntology_url":"https://syntology.ai/paper/2305.03688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03688"}},"official":{"repos":["modelscope/adaseq"],"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/expository-text-generation-imitate-retrieve","slug":"expository-text-generation-imitate-retrieve","title":"Expository Text Generation: Imitate, Retrieve, Paraphrase","date":"2023-05-05","arxiv_id":"2305.03276","repositories_listed":1,"syntology":null},{"url":"/paper/newsquote-a-dataset-built-on-quote-extraction","slug":"newsquote-a-dataset-built-on-quote-extraction","title":"NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking","date":"2023-05-05","arxiv_id":"2305.04825","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/boundary-aware-backward-compatible","slug":"boundary-aware-backward-compatible","title":"Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval","date":"2023-05-04","arxiv_id":"2305.02610","repositories_listed":1,"syntology":null},{"url":"/paper/chain-of-skills-a-configurable-model-for-open","slug":"chain-of-skills-a-configurable-model-for-open","title":"Chain-of-Skills: A Configurable Model for Open-domain Question Answering","date":"2023-05-04","arxiv_id":"2305.03130","repositories_listed":1,"syntology":null},{"url":"/paper/retromae-2-duplex-masked-auto-encoder-for-pre","slug":"retromae-2-duplex-masked-auto-encoder-for-pre","title":"RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models","date":"2023-05-04","arxiv_id":"2305.02564","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":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/retromae-2-duplex-masked-auto-encoder-for-pre#ran","syntology_url":"https://syntology.ai/paper/2305.02564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02564"}},"official":{"repos":["staoxiao/retromae"],"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/a-neural-divide-and-conquer-reasoning","slug":"a-neural-divide-and-conquer-reasoning","title":"A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text","date":"2023-05-03","arxiv_id":"2305.02265","repositories_listed":1,"syntology":null},{"url":"/paper/generating-synthetic-documents-for-cross","slug":"generating-synthetic-documents-for-cross","title":"Generating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts","date":"2023-05-03","arxiv_id":"2305.02320","repositories_listed":1,"syntology":null},{"url":"/paper/gpt-re-in-context-learning-for-relation","slug":"gpt-re-in-context-learning-for-relation","title":"GPT-RE: In-context Learning for Relation Extraction using Large Language Models","date":"2023-05-03","arxiv_id":"2305.02105","repositories_listed":1,"syntology":null},{"url":"/paper/lift-yourself-up-retrieval-augmented-text","slug":"lift-yourself-up-retrieval-augmented-text","title":"Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory","date":"2023-05-03","arxiv_id":"2305.02437","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/lift-yourself-up-retrieval-augmented-text#ran","syntology_url":"https://syntology.ai/paper/2305.02437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02437"}},"official":{"repos":["hannibal046/selfmemory"],"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/quantifying-the-dissimilarity-of-texts","slug":"quantifying-the-dissimilarity-of-texts","title":"Quantifying the Dissimilarity of Texts","date":"2023-05-03","arxiv_id":"2305.02457","repositories_listed":1,"syntology":null},{"url":"/paper/uncovering-chatgpt-s-capabilities-in","slug":"uncovering-chatgpt-s-capabilities-in","title":"Uncovering ChatGPT's Capabilities in Recommender Systems","date":"2023-05-03","arxiv_id":"2305.02182","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/uncovering-chatgpt-s-capabilities-in#ran","syntology_url":"https://syntology.ai/paper/2305.02182","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02182"}},"official":{"repos":["rainym00d/llm4rs"],"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/understanding-differential-search-index-for","slug":"understanding-differential-search-index-for","title":"Understanding Differential Search Index for Text Retrieval","date":"2023-05-03","arxiv_id":"2305.02073","repositories_listed":1,"syntology":null},{"url":"/paper/discern-and-answer-mitigating-the-impact-of","slug":"discern-and-answer-mitigating-the-impact-of","title":"Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise","date":"2023-05-02","arxiv_id":"2305.01579","repositories_listed":1,"syntology":null},{"url":"/paper/huatuo-26m-a-large-scale-chinese-medical-qa","slug":"huatuo-26m-a-large-scale-chinese-medical-qa","title":"Huatuo-26M, a Large-scale Chinese Medical QA Dataset","date":"2023-05-02","arxiv_id":"2305.01526","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-neural-databases","slug":"multimodal-neural-databases","title":"Multimodal Neural Databases","date":"2023-05-02","arxiv_id":"2305.01447","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-guided-traversal-for-fast-learned","slug":"optimizing-guided-traversal-for-fast-learned","title":"Optimizing Guided Traversal for Fast Learned Sparse Retrieval","date":"2023-05-02","arxiv_id":"2305.01203","repositories_listed":1,"syntology":null},{"url":"/paper/tmr-text-to-motion-retrieval-using","slug":"tmr-text-to-motion-retrieval-using","title":"TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis","date":"2023-05-02","arxiv_id":"2305.00976","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":3,"n_instrument":6,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"9 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; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tmr-text-to-motion-retrieval-using#ran","syntology_url":"https://syntology.ai/paper/2305.00976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00976"}},"official":null}},{"url":"/paper/turning-flowchart-into-dialog-plan-based-data","slug":"turning-flowchart-into-dialog-plan-based-data","title":"Turning Flowchart into Dialog: Augmenting Flowchart-grounded Troubleshooting Dialogs via Synthetic Data Generation","date":"2023-05-02","arxiv_id":"2305.01323","repositories_listed":1,"syntology":null},{"url":"/paper/loopy-a-research-friendly-mix-framework-for","slug":"loopy-a-research-friendly-mix-framework-for","title":"LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music","date":"2023-05-01","arxiv_id":"2305.01051","repositories_listed":1,"syntology":null},{"url":"/paper/non-linear-phase-retrieval-algorithms-for-x","slug":"non-linear-phase-retrieval-algorithms-for-x","title":"Maximum Likelihood based Phase-Retrieval using Fresnel Propagation Forward Models with Optional Constraints","date":"2023-04-29","arxiv_id":"2305.00334","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-generative-retriever-for-knowledge","slug":"a-unified-generative-retriever-for-knowledge","title":"A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning","date":"2023-04-28","arxiv_id":"2304.14856","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-study-of-multimodal-model","slug":"an-empirical-study-of-multimodal-model","title":"An Empirical Study of Multimodal Model Merging","date":"2023-04-28","arxiv_id":"2304.14933","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/an-empirical-study-of-multimodal-model#ran","syntology_url":"https://syntology.ai/paper/2304.14933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.14933"}},"official":{"repos":["ylsung/vl-merging"],"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/search-in-the-chain-towards-the-accurate","slug":"search-in-the-chain-towards-the-accurate","title":"Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks","date":"2023-04-28","arxiv_id":"2304.14732","repositories_listed":1,"syntology":null},{"url":"/paper/topic-oriented-adversarial-attacks-against","slug":"topic-oriented-adversarial-attacks-against","title":"Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models","date":"2023-04-28","arxiv_id":"2304.14867","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-based-maximally-interfered-retrieval","slug":"gradient-based-maximally-interfered-retrieval","title":"Gradient-based Maximally Interfered Retrieval for Domain Incremental 3D Object Detection","date":"2023-04-27","arxiv_id":"2304.14460","repositories_listed":1,"syntology":null},{"url":"/paper/a-personalized-dense-retrieval-framework-for","slug":"a-personalized-dense-retrieval-framework-for","title":"A Personalized Dense Retrieval Framework for Unified Information Access","date":"2023-04-26","arxiv_id":"2304.13654","repositories_listed":1,"syntology":null},{"url":"/paper/a-symmetric-dual-encoding-dense-retrieval","slug":"a-symmetric-dual-encoding-dense-retrieval","title":"A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering","date":"2023-04-26","arxiv_id":"2304.13649","repositories_listed":1,"syntology":null},{"url":"/paper/from-association-to-generation-text-only","slug":"from-association-to-generation-text-only","title":"From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping","date":"2023-04-26","arxiv_id":"2304.13273","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-multi-modal-sequential","slug":"self-supervised-multi-modal-sequential","title":"Self-Supervised Multi-Modal Sequential Recommendation","date":"2023-04-26","arxiv_id":"2304.13277","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/unleashing-infinite-length-input-capacity-for","slug":"unleashing-infinite-length-input-capacity-for","title":"Enhancing Large Language Model with Self-Controlled Memory Framework","date":"2023-04-26","arxiv_id":"2304.13343","repositories_listed":1,"syntology":{"n":14,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/unleashing-infinite-length-input-capacity-for#ran","syntology_url":"https://syntology.ai/paper/2304.13343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13343"}},"official":{"repos":["wbbeyourself/scm4llms"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/a-new-inexact-proximal-linear-algorithm-with","slug":"a-new-inexact-proximal-linear-algorithm-with","title":"A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval","date":"2023-04-25","arxiv_id":"2304.12522","repositories_listed":1,"syntology":null},{"url":"/paper/a-preliminary-evaluation-of-chatgpt-in","slug":"a-preliminary-evaluation-of-chatgpt-in","title":"Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting","date":"2023-04-25","arxiv_id":"2304.12562","repositories_listed":1,"syntology":null},{"url":"/paper/explain-like-i-am-bm25-interpreting-a-dense","slug":"explain-like-i-am-bm25-interpreting-a-dense","title":"Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation","date":"2023-04-25","arxiv_id":"2304.12631","repositories_listed":1,"syntology":null},{"url":"/paper/learnable-pillar-based-re-ranking-for-image","slug":"learnable-pillar-based-re-ranking-for-image","title":"Learnable Pillar-based Re-ranking for Image-Text Retrieval","date":"2023-04-25","arxiv_id":"2304.12570","repositories_listed":1,"syntology":null},{"url":"/paper/sebis-at-semeval-2023-task-7-a-joint-system","slug":"sebis-at-semeval-2023-task-7-a-joint-system","title":"Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports","date":"2023-04-25","arxiv_id":"2304.13180","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-tree-based-index-for-efficient","slug":"constructing-tree-based-index-for-efficient","title":"Constructing Tree-based Index for Efficient and Effective Dense Retrieval","date":"2023-04-24","arxiv_id":"2304.11943","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":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) · 1 unverified","sample_list":"/paper/constructing-tree-based-index-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2304.11943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11943"}},"official":{"repos":["cshaitao/jtr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rank-flow-embedding-for-unsupervised-and-semi-1","slug":"rank-flow-embedding-for-unsupervised-and-semi-1","title":"Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning","date":"2023-04-24","arxiv_id":"2304.12448","repositories_listed":1,"syntology":null},{"url":"/paper/a-lightweight-constrained-generation","slug":"a-lightweight-constrained-generation","title":"A Lightweight Constrained Generation Alternative for Query-focused Summarization","date":"2023-04-23","arxiv_id":"2304.11721","repositories_listed":1,"syntology":null},{"url":"/paper/logicrec-recommendation-with-users-logical","slug":"logicrec-recommendation-with-users-logical","title":"LogicRec: Recommendation with Users' Logical Requirements","date":"2023-04-23","arxiv_id":"2304.11722","repositories_listed":1,"syntology":null},{"url":"/paper/uhrnet-a-deep-learning-based-method-for","slug":"uhrnet-a-deep-learning-based-method-for","title":"UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from a Single Fringe-Pattern","date":"2023-04-23","arxiv_id":"2304.14503","repositories_listed":1,"syntology":null},{"url":"/paper/sailer-structure-aware-pre-trained-language","slug":"sailer-structure-aware-pre-trained-language","title":"SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval","date":"2023-04-22","arxiv_id":"2304.11370","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sailer-structure-aware-pre-trained-language#ran","syntology_url":"https://syntology.ai/paper/2304.11370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11370"}},"official":{"repos":["cshaitao/sailer"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-benchmarks-for-cross-modal-image","slug":"rethinking-benchmarks-for-cross-modal-image","title":"Rethinking Benchmarks for Cross-modal Image-text Retrieval","date":"2023-04-21","arxiv_id":"2304.10824","repositories_listed":1,"syntology":null},{"url":"/paper/rococo-robust-benchmark-ms-coco-to-stress","slug":"rococo-robust-benchmark-ms-coco-to-stress","title":"RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models","date":"2023-04-21","arxiv_id":"2304.10727","repositories_listed":1,"syntology":null},{"url":"/paper/image-text-retrieval-via-preserving-main","slug":"image-text-retrieval-via-preserving-main","title":"Image-text Retrieval via Preserving Main Semantics of Vision","date":"2023-04-20","arxiv_id":"2304.10254","repositories_listed":1,"syntology":null},{"url":"/paper/brent-bidirectional-retrieval-enhanced","slug":"brent-bidirectional-retrieval-enhanced","title":"BRENT: Bidirectional Retrieval Enhanced Norwegian Transformer","date":"2023-04-19","arxiv_id":"2304.09649","repositories_listed":1,"syntology":null},{"url":"/paper/genegpt-teaching-large-language-models-to-use","slug":"genegpt-teaching-large-language-models-to-use","title":"GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information","date":"2023-04-19","arxiv_id":"2304.09667","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/genegpt-teaching-large-language-models-to-use#ran","syntology_url":"https://syntology.ai/paper/2304.09667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.09667"}},"official":{"repos":["ncbi/GeneGPT"],"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/is-chatgpt-good-at-search-investigating-large","slug":"is-chatgpt-good-at-search-investigating-large","title":"Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents","date":"2023-04-19","arxiv_id":"2304.09542","repositories_listed":1,"syntology":null},{"url":"/paper/aircade-an-anechoic-and-ir-convolution-based","slug":"aircade-an-anechoic-and-ir-convolution-based","title":"AIRCADE: an Anechoic and IR Convolution-based Auralization Data-compilation Ensemble","date":"2023-04-18","arxiv_id":"2304.09318","repositories_listed":1,"syntology":null},{"url":"/paper/phase-retrieval-with-incomplete","slug":"phase-retrieval-with-incomplete","title":"Phase-Retrieval with Incomplete Autocorrelations Using Deep Convolutional Autoencoders","date":"2023-04-18","arxiv_id":"2304.09303","repositories_listed":1,"syntology":null},{"url":"/paper/svitt-temporal-learning-of-sparse-video-text","slug":"svitt-temporal-learning-of-sparse-video-text","title":"SViTT: Temporal Learning of Sparse Video-Text Transformers","date":"2023-04-18","arxiv_id":"2304.08809","repositories_listed":1,"syntology":null},{"url":"/paper/ptc-net-point-wise-transformer-with-sparse","slug":"ptc-net-point-wise-transformer-with-sparse","title":"PTC-Net: Point-Wise Transformer with Sparse Convolution Network for Place Recognition","date":"2023-04-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/typos-aware-bottlenecked-pre-training-for","slug":"typos-aware-bottlenecked-pre-training-for","title":"Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval","date":"2023-04-17","arxiv_id":"2304.08138","repositories_listed":1,"syntology":null}],"record_sha256":"e43599a8541cb5d66a246099c9c0540ace6f9342d45920498f09963005355258","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}