{"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-augmented-generation/papers/7","list_of":"/task/retrieval-augmented-generation","task":"Retrieval-augmented Generation","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":7,"pages_in_order":22,"rows_per_page":100,"rows":[601,700],"of":2196,"counts":{"archive_papers_tagged":2196,"with_a_code_link":777,"where_syntology_ran_a_sample":218,"not_listed_spam_title":0,"listed":2196,"listed_where_code_ran":218,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":176,"every_run_a_failure_of_syntologys_instrument":42,"listed_with_a_run_with_no_instrument_failure":176,"listed_every_run_a_failure_of_syntologys_instrument":42,"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-augmented-generation","prev":"/task/retrieval-augmented-generation/papers/6","next":"/task/retrieval-augmented-generation/papers/8","papers":[{"url":"/paper/augmenting-query-and-passage-for-retrieval","slug":"augmenting-query-and-passage-for-retrieval","title":"QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs","date":"2024-06-20","arxiv_id":"2406.14277","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/augmenting-query-and-passage-for-retrieval#ran","syntology_url":"https://syntology.ai/paper/2406.14277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14277"}},"official":{"repos":["kmswin1/qpaug"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/coderag-bench-can-retrieval-augment-code","slug":"coderag-bench-can-retrieval-augment-code","title":"CodeRAG-Bench: Can Retrieval Augment Code Generation?","date":"2024-06-20","arxiv_id":"2406.14497","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/coderag-bench-can-retrieval-augment-code#ran","syntology_url":"https://syntology.ai/paper/2406.14497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14497"}},"official":{"repos":["code-rag-bench/code-rag-bench"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diras-efficient-llm-assisted-annotation-of","slug":"diras-efficient-llm-assisted-annotation-of","title":"DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation","date":"2024-06-20","arxiv_id":"2406.14162","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-rag-fusion-with-ragelo-an","slug":"evaluating-rag-fusion-with-ragelo-an","title":"Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework","date":"2024-06-20","arxiv_id":"2406.14783","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":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) · 1 unverified","sample_list":"/paper/evaluating-rag-fusion-with-ragelo-an#ran","syntology_url":"https://syntology.ai/paper/2406.14783","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14783"}},"official":{"repos":["zetaalphavector/ragelo"],"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/instructrag-instructing-retrieval-augmented","slug":"instructrag-instructing-retrieval-augmented","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","date":"2024-06-19","arxiv_id":"2406.13629","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":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) · 1 unverified","sample_list":"/paper/instructrag-instructing-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.13629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13629"}},"official":{"repos":["weizhepei/instructrag"],"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/model-internals-based-answer-attribution-for","slug":"model-internals-based-answer-attribution-for","title":"Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13663","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/model-internals-based-answer-attribution-for#ran","syntology_url":"https://syntology.ai/paper/2406.13663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13663"}},"official":{"repos":["betswish/mirage"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-meta-rag-improving-rag-for-multi-hop","slug":"multi-meta-rag-improving-rag-for-multi-hop","title":"Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata","date":"2024-06-19","arxiv_id":"2406.13213","repositories_listed":1,"syntology":null},{"url":"/paper/r-2ag-incorporating-retrieval-information","slug":"r-2ag-incorporating-retrieval-information","title":"R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13249","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/r-2ag-incorporating-retrieval-information#ran","syntology_url":"https://syntology.ai/paper/2406.13249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13249"}},"official":{"repos":["yefd/RRAG"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stackrag-agent-improving-developer-answers","slug":"stackrag-agent-improving-developer-answers","title":"StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13840","repositories_listed":1,"syntology":null},{"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/planrag-a-plan-then-retrieval-augmented","slug":"planrag-a-plan-then-retrieval-augmented","title":"PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers","date":"2024-06-18","arxiv_id":"2406.12430","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/planrag-a-plan-then-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.12430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12430"}},"official":{"repos":["myeon9h/planrag"],"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/unified-active-retrieval-for-retrieval","slug":"unified-active-retrieval-for-retrieval","title":"Unified Active Retrieval for Retrieval Augmented Generation","date":"2024-06-18","arxiv_id":"2406.12534","repositories_listed":1,"syntology":null},{"url":"/paper/cram-credibility-aware-attention-modification","slug":"cram-credibility-aware-attention-modification","title":"CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG","date":"2024-06-17","arxiv_id":"2406.11497","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-the-efficacy-of-open-source-llms","slug":"evaluating-the-efficacy-of-open-source-llms","title":"Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability","date":"2024-06-17","arxiv_id":"2406.11424","repositories_listed":1,"syntology":null},{"url":"/paper/language-modeling-with-editable-external","slug":"language-modeling-with-editable-external","title":"Language Modeling with Editable External Knowledge","date":"2024-06-17","arxiv_id":"2406.11830","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/language-modeling-with-editable-external#ran","syntology_url":"https://syntology.ai/paper/2406.11830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11830"}},"official":{"repos":["belindal/erase"],"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/satyrn-a-platform-for-analytics-augmented","slug":"satyrn-a-platform-for-analytics-augmented","title":"Satyrn: A Platform for Analytics Augmented Generation","date":"2024-06-17","arxiv_id":"2406.12069","repositories_listed":1,"syntology":null},{"url":"/paper/textit-refiner-restructure-retrieval-content","slug":"textit-refiner-restructure-retrieval-content","title":"Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities","date":"2024-06-17","arxiv_id":"2406.11357","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":8,"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/textit-refiner-restructure-retrieval-content#ran","syntology_url":"https://syntology.ai/paper/2406.11357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11357"}},"official":{"repos":["allen-li1231/refiner-rag"],"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/climretrieve-a-benchmarking-dataset-for","slug":"climretrieve-a-benchmarking-dataset-for","title":"ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures","date":"2024-06-14","arxiv_id":"2406.09818","repositories_listed":1,"syntology":null},{"url":"/paper/hiro-hierarchical-information-retrieval","slug":"hiro-hierarchical-information-retrieval","title":"HIRO: Hierarchical Information Retrieval Optimization","date":"2024-06-14","arxiv_id":"2406.09979","repositories_listed":1,"syntology":null},{"url":"/paper/rs-agent-automating-remote-sensing-tasks","slug":"rs-agent-automating-remote-sensing-tasks","title":"RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent","date":"2024-06-11","arxiv_id":"2406.07089","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/rs-agent-automating-remote-sensing-tasks#ran","syntology_url":"https://syntology.ai/paper/2406.07089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07089"}},"official":{"repos":["intellisensing/rs-agent"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scholarly-question-answering-using-large","slug":"scholarly-question-answering-using-large","title":"Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway","date":"2024-06-11","arxiv_id":"2406.07257","repositories_listed":1,"syntology":null},{"url":"/paper/should-we-fine-tune-or-rag-evaluating","slug":"should-we-fine-tune-or-rag-evaluating","title":"Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for Dialogue","date":"2024-06-10","arxiv_id":"2406.06399","repositories_listed":1,"syntology":null},{"url":"/paper/re-rag-improving-open-domain-qa-performance","slug":"re-rag-improving-open-domain-qa-performance","title":"RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation","date":"2024-06-09","arxiv_id":"2406.05794","repositories_listed":1,"syntology":null},{"url":"/paper/corpus-poisoning-via-approximate-greedy","slug":"corpus-poisoning-via-approximate-greedy","title":"Corpus Poisoning via Approximate Greedy Gradient Descent","date":"2024-06-07","arxiv_id":"2406.05087","repositories_listed":1,"syntology":null},{"url":"/paper/analyzing-temporal-complex-events-with-large","slug":"analyzing-temporal-complex-events-with-large","title":"Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding","date":"2024-06-04","arxiv_id":"2406.02472","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/analyzing-temporal-complex-events-with-large#ran","syntology_url":"https://syntology.ai/paper/2406.02472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02472"}},"official":{"repos":["Zhihan72/TCELongBench"],"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/ratt-athought-structure-for-coherent-and","slug":"ratt-athought-structure-for-coherent-and","title":"RATT: A Thought Structure for Coherent and Correct LLM Reasoning","date":"2024-06-04","arxiv_id":"2406.02746","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/ratt-athought-structure-for-coherent-and#ran","syntology_url":"https://syntology.ai/paper/2406.02746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02746"}},"official":{"repos":["jinghanzhang1998/ratt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/an-information-bottleneck-perspective-for","slug":"an-information-bottleneck-perspective-for","title":"An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation","date":"2024-06-03","arxiv_id":"2406.01549","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-information-bottleneck-perspective-for#ran","syntology_url":"https://syntology.ai/paper/2406.01549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01549"}},"official":{"repos":["zhukun1020/noisefilter_ib"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/demo-soccer-information-retrieval-via-natural","slug":"demo-soccer-information-retrieval-via-natural","title":"Demo: Soccer Information Retrieval via Natural Queries using SoccerRAG","date":"2024-06-03","arxiv_id":"2406.01280","repositories_listed":1,"syntology":null},{"url":"/paper/soccerrag-multimodal-soccer-information","slug":"soccerrag-multimodal-soccer-information","title":"SoccerRAG: Multimodal Soccer Information Retrieval via Natural Queries","date":"2024-06-03","arxiv_id":"2406.01273","repositories_listed":1,"syntology":null},{"url":"/paper/cos-mix-cosine-similarity-and-distance-fusion","slug":"cos-mix-cosine-similarity-and-distance-fusion","title":"COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval","date":"2024-06-02","arxiv_id":"2406.00638","repositories_listed":1,"syntology":null},{"url":"/paper/mix-of-granularity-optimize-the-chunking","slug":"mix-of-granularity-optimize-the-chunking","title":"Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation","date":"2024-06-01","arxiv_id":"2406.00456","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-noise-robustness-of-retrieval","slug":"enhancing-noise-robustness-of-retrieval","title":"Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training","date":"2024-05-31","arxiv_id":"2405.20978","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/enhancing-noise-robustness-of-retrieval#ran","syntology_url":"https://syntology.ai/paper/2405.20978","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20978"}},"official":{"repos":["calubkk/raat"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/designing-an-evaluation-framework-for-large","slug":"designing-an-evaluation-framework-for-large","title":"Designing an Evaluation Framework for Large Language Models in Astronomy Research","date":"2024-05-30","arxiv_id":"2405.20389","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/designing-an-evaluation-framework-for-large#ran","syntology_url":"https://syntology.ai/paper/2405.20389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20389"}},"official":{"repos":["jsalt2024-evaluating-llms-for-astronomy/astro-arxiv-bot"],"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/gnn-rag-graph-neural-retrieval-for-large","slug":"gnn-rag-graph-neural-retrieval-for-large","title":"GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning","date":"2024-05-30","arxiv_id":"2405.20139","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"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) · 1 unverified","sample_list":"/paper/gnn-rag-graph-neural-retrieval-for-large#ran","syntology_url":"https://syntology.ai/paper/2405.20139","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.20139"}},"official":{"repos":["cmavro/gnn-rag"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ctrla-adaptive-retrieval-augmented-generation","slug":"ctrla-adaptive-retrieval-augmented-generation","title":"CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control","date":"2024-05-29","arxiv_id":"2405.18727","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/ctrla-adaptive-retrieval-augmented-generation#ran","syntology_url":"https://syntology.ai/paper/2405.18727","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18727"}},"official":{"repos":["hsliu-initial/ctrla"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/toward-conversational-agents-with-context-and","slug":"toward-conversational-agents-with-context-and","title":"Toward Conversational Agents with Context and Time Sensitive Long-term Memory","date":"2024-05-29","arxiv_id":"2406.00057","repositories_listed":1,"syntology":null},{"url":"/paper/atm-adversarial-tuning-multi-agent-system","slug":"atm-adversarial-tuning-multi-agent-system","title":"ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator","date":"2024-05-28","arxiv_id":"2405.18111","repositories_listed":1,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"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) · 4 unverified","sample_list":"/paper/atm-adversarial-tuning-multi-agent-system#ran","syntology_url":"https://syntology.ai/paper/2405.18111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18111"}},"official":{"repos":["chuhac/atm-rag"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/empowering-large-language-models-to-set-up-a","slug":"empowering-large-language-models-to-set-up-a","title":"Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning","date":"2024-05-27","arxiv_id":"2405.16933","repositories_listed":1,"syntology":null},{"url":"/paper/video-enriched-retrieval-augmented-generation","slug":"video-enriched-retrieval-augmented-generation","title":"Video Enriched Retrieval Augmented Generation Using Aligned Video Captions","date":"2024-05-27","arxiv_id":"2405.17706","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-semantic-integrator-esi-a-foundation-ecg","slug":"ecg-semantic-integrator-esi-a-foundation-ecg","title":"ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text","date":"2024-05-26","arxiv_id":"2405.19366","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":9,"n_pointer_only":14,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 2 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ecg-semantic-integrator-esi-a-foundation-ecg#ran","syntology_url":"https://syntology.ai/paper/2405.19366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19366"}},"official":{"repos":["comp-well-org/esi"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/grag-graph-retrieval-augmented-generation","slug":"grag-graph-retrieval-augmented-generation","title":"GRAG: Graph Retrieval-Augmented Generation","date":"2024-05-26","arxiv_id":"2405.16506","repositories_listed":1,"syntology":null},{"url":"/paper/synthai-a-multi-agent-generative-ai-framework","slug":"synthai-a-multi-agent-generative-ai-framework","title":"SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation","date":"2024-05-25","arxiv_id":"2405.16072","repositories_listed":1,"syntology":null},{"url":"/paper/certifiably-robust-rag-against-retrieval","slug":"certifiably-robust-rag-against-retrieval","title":"Certifiably Robust RAG against Retrieval Corruption","date":"2024-05-24","arxiv_id":"2405.15556","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-context-retrieval-augmented-generation","slug":"hybrid-context-retrieval-augmented-generation","title":"Hybrid Context Retrieval Augmented Generation Pipeline: LLM-Augmented Knowledge Graphs and Vector Database for Accreditation Reporting Assistance","date":"2024-05-24","arxiv_id":"2405.15436","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-reflect-human-citation","slug":"large-language-models-reflect-human-citation","title":"Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias","date":"2024-05-24","arxiv_id":"2405.15739","repositories_listed":1,"syntology":null},{"url":"/paper/g3-an-effective-and-adaptive-framework-for","slug":"g3-an-effective-and-adaptive-framework-for","title":"G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models","date":"2024-05-23","arxiv_id":"2405.14702","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/g3-an-effective-and-adaptive-framework-for#ran","syntology_url":"https://syntology.ai/paper/2405.14702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14702"}},"official":{"repos":["applied-machine-learning-lab/g3"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/flashrag-a-modular-toolkit-for-efficient","slug":"flashrag-a-modular-toolkit-for-efficient","title":"FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research","date":"2024-05-22","arxiv_id":"2405.13576","repositories_listed":1,"syntology":null},{"url":"/paper/trojanrag-retrieval-augmented-generation-can","slug":"trojanrag-retrieval-augmented-generation-can","title":"TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models","date":"2024-05-22","arxiv_id":"2405.13401","repositories_listed":1,"syntology":null},{"url":"/paper/xrag-extreme-context-compression-for","slug":"xrag-extreme-context-compression-for","title":"xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token","date":"2024-05-22","arxiv_id":"2405.13792","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/xrag-extreme-context-compression-for#ran","syntology_url":"https://syntology.ai/paper/2405.13792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.13792"}},"official":{"repos":["Hannibal046/xRAG"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"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/the-2nd-futuredial-challenge-dialog-systems","slug":"the-2nd-futuredial-challenge-dialog-systems","title":"The 2nd FutureDial Challenge: Dialog Systems with Retrieval Augmented Generation (FutureDial-RAG)","date":"2024-05-21","arxiv_id":"2405.13084","repositories_listed":1,"syntology":null},{"url":"/paper/can-github-issues-be-solved-with-tree-of","slug":"can-github-issues-be-solved-with-tree-of","title":"Can Github issues be solved with Tree Of Thoughts?","date":"2024-05-20","arxiv_id":"2405.13057","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-retrieval-augmented-generation","slug":"evaluation-of-retrieval-augmented-generation","title":"Evaluation of Retrieval-Augmented Generation: A Survey","date":"2024-05-13","arxiv_id":"2405.07437","repositories_listed":1,"syntology":null},{"url":"/paper/eragent-enhancing-retrieval-augmented","slug":"eragent-enhancing-retrieval-augmented","title":"ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization","date":"2024-05-06","arxiv_id":"2405.06683","repositories_listed":1,"syntology":null},{"url":"/paper/biomedrag-a-retrieval-augmented-large","slug":"biomedrag-a-retrieval-augmented-large","title":"BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine","date":"2024-05-01","arxiv_id":"2405.00465","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":4,"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/biomedrag-a-retrieval-augmented-large#ran","syntology_url":"https://syntology.ai/paper/2405.00465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.00465"}},"official":{"repos":["toneli/petailor-for-bio-triple-extraction"],"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/fine-tuning-and-retrieval-augmented","slug":"fine-tuning-and-retrieval-augmented","title":"Fine-Tuning and Retrieval Augmented Generation for Question Answering Using Affordable Large Language Models","date":"2024-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/grammar-grounded-and-modular-evaluation-of","slug":"grammar-grounded-and-modular-evaluation-of","title":"GRAMMAR: Grounded and Modular Methodology for Assessment of Closed-Domain Retrieval-Augmented Language Model","date":"2024-04-30","arxiv_id":"2404.19232","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-search-engine-for-machines-unified","slug":"towards-a-search-engine-for-machines-unified","title":"Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models","date":"2024-04-30","arxiv_id":"2405.00175","repositories_listed":1,"syntology":null},{"url":"/paper/studying-large-language-model-behaviors-under","slug":"studying-large-language-model-behaviors-under","title":"Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents","date":"2024-04-24","arxiv_id":"2404.16032","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/studying-large-language-model-behaviors-under#ran","syntology_url":"https://syntology.ai/paper/2404.16032","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16032"}},"official":{"repos":["kortukov/realistic_knowledge_conflicts"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/telco-rag-navigating-the-challenges-of","slug":"telco-rag-navigating-the-challenges-of","title":"Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications","date":"2024-04-24","arxiv_id":"2404.15939","repositories_listed":1,"syntology":null},{"url":"/paper/boter-bootstrapping-knowledge-selection-and","slug":"boter-bootstrapping-knowledge-selection-and","title":"Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering","date":"2024-04-22","arxiv_id":"2404.13947","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"5 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/boter-bootstrapping-knowledge-selection-and#ran","syntology_url":"https://syntology.ai/paper/2404.13947","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13947"}},"official":{"repos":["haodongze/self-ksel-qans"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/llms-know-what-they-need-leveraging-a-missing","slug":"llms-know-what-they-need-leveraging-a-missing","title":"LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation","date":"2024-04-22","arxiv_id":"2404.14043","repositories_listed":1,"syntology":null},{"url":"/paper/typos-that-broke-the-rag-s-back-genetic","slug":"typos-that-broke-the-rag-s-back-genetic","title":"Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations","date":"2024-04-22","arxiv_id":"2404.13948","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/typos-that-broke-the-rag-s-back-genetic#ran","syntology_url":"https://syntology.ai/paper/2404.13948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13948"}},"official":{"repos":["zomss/garag"],"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/evaluating-retrieval-quality-in-retrieval","slug":"evaluating-retrieval-quality-in-retrieval","title":"Evaluating Retrieval Quality in Retrieval-Augmented Generation","date":"2024-04-21","arxiv_id":"2404.13781","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evaluating-retrieval-quality-in-retrieval#ran","syntology_url":"https://syntology.ai/paper/2404.13781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.13781"}},"official":{"repos":["alirezasalemi7/erag"],"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/retrieval-augmented-generation-based-relation","slug":"retrieval-augmented-generation-based-relation","title":"Retrieval-Augmented Generation-based Relation Extraction","date":"2024-04-20","arxiv_id":"2404.13397","repositories_listed":1,"syntology":null},{"url":"/paper/dubo-sql-diverse-retrieval-augmented","slug":"dubo-sql-diverse-retrieval-augmented","title":"Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL","date":"2024-04-19","arxiv_id":"2404.12560","repositories_listed":1,"syntology":null},{"url":"/paper/how-faithful-are-rag-models-quantifying-the","slug":"how-faithful-are-rag-models-quantifying-the","title":"ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence","date":"2024-04-16","arxiv_id":"2404.10198","repositories_listed":1,"syntology":null},{"url":"/paper/spiral-of-silences-how-is-large-language","slug":"spiral-of-silences-how-is-large-language","title":"Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering","date":"2024-04-16","arxiv_id":"2404.10496","repositories_listed":1,"syntology":null},{"url":"/paper/llms-in-biomedicine-a-study-on-clinical-named","slug":"llms-in-biomedicine-a-study-on-clinical-named","title":"LLMs in Biomedicine: A study on clinical Named Entity Recognition","date":"2024-04-10","arxiv_id":"2404.07376","repositories_listed":1,"syntology":null},{"url":"/paper/not-all-contexts-are-equal-teaching-llms","slug":"not-all-contexts-are-equal-teaching-llms","title":"Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation","date":"2024-04-10","arxiv_id":"2404.06809","repositories_listed":1,"syntology":null},{"url":"/paper/superposition-prompting-improving-and","slug":"superposition-prompting-improving-and","title":"Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation","date":"2024-04-10","arxiv_id":"2404.06910","repositories_listed":1,"syntology":null},{"url":"/paper/aisaq-all-in-storage-anns-with-product","slug":"aisaq-all-in-storage-anns-with-product","title":"AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval","date":"2024-04-09","arxiv_id":"2404.06004","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/iitk-at-semeval-2024-task-2-exploring-the","slug":"iitk-at-semeval-2024-task-2-exploring-the","title":"IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical Trials","date":"2024-04-06","arxiv_id":"2404.04510","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparison-of-methods-for-evaluating","slug":"a-comparison-of-methods-for-evaluating","title":"A Comparison of Methods for Evaluating Generative IR","date":"2024-04-05","arxiv_id":"2404.04044","repositories_listed":1,"syntology":null},{"url":"/paper/extract-define-canonicalize-an-llm-based","slug":"extract-define-canonicalize-an-llm-based","title":"Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction","date":"2024-04-05","arxiv_id":"2404.03868","repositories_listed":1,"syntology":null},{"url":"/paper/cbr-rag-case-based-reasoning-for-retrieval","slug":"cbr-rag-case-based-reasoning-for-retrieval","title":"CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering","date":"2024-04-04","arxiv_id":"2404.04302","repositories_listed":1,"syntology":null},{"url":"/paper/conflare-conformal-large-language-model","slug":"conflare-conformal-large-language-model","title":"CONFLARE: CONFormal LArge language model REtrieval","date":"2024-04-04","arxiv_id":"2404.04287","repositories_listed":1,"syntology":null},{"url":"/paper/utebc-nlp-at-semeval-2024-task-9-can-llms-be","slug":"utebc-nlp-at-semeval-2024-task-9-can-llms-be","title":"uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?","date":"2024-04-03","arxiv_id":"2404.02474","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/utebc-nlp-at-semeval-2024-task-9-can-llms-be#ran","syntology_url":"https://syntology.ai/paper/2404.02474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.02474"}},"official":{"repos":["ipouyall/can-llms-be-lateral-thinkers"],"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/clapnq-cohesive-long-form-answers-from","slug":"clapnq-cohesive-long-form-answers-from","title":"CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems","date":"2024-04-02","arxiv_id":"2404.02103","repositories_listed":1,"syntology":null},{"url":"/paper/improving-retrieval-augmented-open-domain","slug":"improving-retrieval-augmented-open-domain","title":"Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts","date":"2024-04-02","arxiv_id":"2404.02022","repositories_listed":1,"syntology":null},{"url":"/paper/prompts-as-programs-a-structure-aware","slug":"prompts-as-programs-a-structure-aware","title":"Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization","date":"2024-04-02","arxiv_id":"2404.02319","repositories_listed":1,"syntology":null},{"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/rq-rag-learning-to-refine-queries-for","slug":"rq-rag-learning-to-refine-queries-for","title":"RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation","date":"2024-03-31","arxiv_id":"2404.00610","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-robust-retrieval-based","slug":"towards-a-robust-retrieval-based","title":"Towards a Robust Retrieval-Based Summarization System","date":"2024-03-29","arxiv_id":"2403.19889","repositories_listed":1,"syntology":null},{"url":"/paper/are-large-language-models-good-at-utility","slug":"are-large-language-models-good-at-utility","title":"Are Large Language Models Good at Utility Judgments?","date":"2024-03-28","arxiv_id":"2403.19216","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"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) · 4 unverified","sample_list":"/paper/are-large-language-models-good-at-utility#ran","syntology_url":"https://syntology.ai/paper/2403.19216","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19216"}},"official":{"repos":["ict-bigdatalab/utility_judgments"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/img2loc-revisiting-image-geolocalization","slug":"img2loc-revisiting-image-geolocalization","title":"Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation","date":"2024-03-28","arxiv_id":"2403.19584","repositories_listed":1,"syntology":null},{"url":"/paper/generation-of-asset-administration-shell-with","slug":"generation-of-asset-administration-shell-with","title":"Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0","date":"2024-03-25","arxiv_id":"2403.17209","repositories_listed":1,"syntology":null},{"url":"/paper/lexdrafter-terminology-drafting-for","slug":"lexdrafter-terminology-drafting-for","title":"LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation","date":"2024-03-24","arxiv_id":"2403.16295","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-textbf-rag-based-summarization","slug":"towards-a-textbf-rag-based-summarization","title":"Towards a RAG-based Summarization Agent for the Electron-Ion Collider","date":"2024-03-23","arxiv_id":"2403.15729","repositories_listed":1,"syntology":null},{"url":"/paper/blended-rag-improving-rag-retriever-augmented","slug":"blended-rag-improving-rag-retriever-augmented","title":"Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers","date":"2024-03-22","arxiv_id":"2404.07220","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/blended-rag-improving-rag-retriever-augmented#ran","syntology_url":"https://syntology.ai/paper/2404.07220","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07220"}},"official":{"repos":["ibm-ecosystem-engineering/blended-rag"],"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/imagination-augmented-generation-learning-to","slug":"imagination-augmented-generation-learning-to","title":"Awakening Augmented Generation: Learning to Awaken Internal Knowledge of Large Language Models for Question Answering","date":"2024-03-22","arxiv_id":"2403.15268","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/imagination-augmented-generation-learning-to#ran","syntology_url":"https://syntology.ai/paper/2403.15268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15268"}},"official":{"repos":["xnhyacinth/iag"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dp-rdm-adapting-diffusion-models-to-private","slug":"dp-rdm-adapting-diffusion-models-to-private","title":"DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning","date":"2024-03-21","arxiv_id":"2403.14421","repositories_listed":1,"syntology":null},{"url":"/paper/alphafin-benchmarking-financial-analysis-with","slug":"alphafin-benchmarking-financial-analysis-with","title":"AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework","date":"2024-03-19","arxiv_id":"2403.12582","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/alphafin-benchmarking-financial-analysis-with#ran","syntology_url":"https://syntology.ai/paper/2403.12582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.12582"}},"official":{"repos":["alphafin-proj/alphafin"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/jora-jax-tensor-parallel-lora-library-for","slug":"jora-jax-tensor-parallel-lora-library-for","title":"JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning","date":"2024-03-17","arxiv_id":"2403.11366","repositories_listed":1,"syntology":null},{"url":"/paper/dragin-dynamic-retrieval-augmented-generation","slug":"dragin-dynamic-retrieval-augmented-generation","title":"DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models","date":"2024-03-15","arxiv_id":"2403.10081","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dragin-dynamic-retrieval-augmented-generation#ran","syntology_url":"https://syntology.ai/paper/2403.10081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10081"}},"official":{"repos":["oneal2000/dragin"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-llm-factual-accuracy-with-rag-to","slug":"enhancing-llm-factual-accuracy-with-rag-to","title":"Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases","date":"2024-03-15","arxiv_id":"2403.10446","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-llm-factual-accuracy-with-rag-to#ran","syntology_url":"https://syntology.ai/paper/2403.10446","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10446"}},"official":{"repos":["anlp-team/LTI_Neural_Navigator"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-medical-multi-modal-contrastive","slug":"improving-medical-multi-modal-contrastive","title":"Improving Medical Multi-modal Contrastive Learning with Expert Annotations","date":"2024-03-15","arxiv_id":"2403.10153","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":12,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/improving-medical-multi-modal-contrastive#ran","syntology_url":"https://syntology.ai/paper/2403.10153","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10153"}},"official":{"repos":["ykumards/eclip"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/s3llm-large-scale-scientific-software","slug":"s3llm-large-scale-scientific-software","title":"S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document","date":"2024-03-15","arxiv_id":"2403.10588","repositories_listed":1,"syntology":null},{"url":"/paper/socialgenpod-privacy-friendly-generative-ai","slug":"socialgenpod-privacy-friendly-generative-ai","title":"SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores","date":"2024-03-15","arxiv_id":"2403.10408","repositories_listed":1,"syntology":null}],"record_sha256":"a32238a379825649000cd1ed74d7ff441767e55db6e3865251c46721da443240","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}