{"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/8","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":8,"pages_in_order":22,"rows_per_page":100,"rows":[701,800],"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/7","next":"/task/retrieval-augmented-generation/papers/9","papers":[{"url":"/paper/ragged-towards-informed-design-of-retrieval","slug":"ragged-towards-informed-design-of-retrieval","title":"RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems","date":"2024-03-14","arxiv_id":"2403.09040","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"phrase":"13 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ragged-towards-informed-design-of-retrieval#ran","syntology_url":"https://syntology.ai/paper/2403.09040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.09040"}},"official":{"repos":["neulab/ragged"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-augmented-text-to-sql-generation","slug":"retrieval-augmented-text-to-sql-generation","title":"Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records","date":"2024-03-14","arxiv_id":"2403.09226","repositories_listed":1,"syntology":null},{"url":"/paper/from-human-experts-to-machines-an-llm","slug":"from-human-experts-to-machines-an-llm","title":"From human experts to machines: An LLM supported approach to ontology and knowledge graph construction","date":"2024-03-13","arxiv_id":"2403.08345","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-performance-of-retrieval","slug":"investigating-the-performance-of-retrieval","title":"Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems","date":"2024-03-12","arxiv_id":"2403.09727","repositories_listed":1,"syntology":null},{"url":"/paper/ra-isf-learning-to-answer-and-understand-from","slug":"ra-isf-learning-to-answer-and-understand-from","title":"RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback","date":"2024-03-11","arxiv_id":"2403.06840","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"8 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/ra-isf-learning-to-answer-and-understand-from#ran","syntology_url":"https://syntology.ai/paper/2403.06840","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06840"}},"official":{"repos":["oceanntwt/ra-isf"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-recommendation-via-hybrid-retrieval","slug":"federated-recommendation-via-hybrid-retrieval","title":"Federated Recommendation via Hybrid Retrieval Augmented Generation","date":"2024-03-07","arxiv_id":"2403.04256","repositories_listed":1,"syntology":null},{"url":"/paper/halueval-wild-evaluating-hallucinations-of","slug":"halueval-wild-evaluating-hallucinations-of","title":"HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild","date":"2024-03-07","arxiv_id":"2403.04307","repositories_listed":1,"syntology":null},{"url":"/paper/faaf-facts-as-a-function-for-the-evaluation","slug":"faaf-facts-as-a-function-for-the-evaluation","title":"FaaF: Facts as a Function for the evaluation of generated text","date":"2024-03-06","arxiv_id":"2403.03888","repositories_listed":1,"syntology":null},{"url":"/paper/neural-exec-learning-and-learning-from","slug":"neural-exec-learning-and-learning-from","title":"Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks","date":"2024-03-06","arxiv_id":"2403.03792","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/neural-exec-learning-and-learning-from#ran","syntology_url":"https://syntology.ai/paper/2403.03792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03792"}},"official":{"repos":["pasquini-dario/llm_neuralexec"],"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/fine-tuning-vs-retrieval-augmented-generation","slug":"fine-tuning-vs-retrieval-augmented-generation","title":"Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge","date":"2024-03-03","arxiv_id":"2403.01432","repositories_listed":1,"syntology":{"n":19,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":19,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fine-tuning-vs-retrieval-augmented-generation#ran","syntology_url":"https://syntology.ai/paper/2403.01432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01432"}},"official":{"repos":["heydarsoudani/ragvsft"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/syllabusqa-a-course-logistics-question","slug":"syllabusqa-a-course-logistics-question","title":"SyllabusQA: A Course Logistics Question Answering Dataset","date":"2024-03-03","arxiv_id":"2403.14666","repositories_listed":1,"syntology":null},{"url":"/paper/rnns-are-not-transformers-yet-the-key","slug":"rnns-are-not-transformers-yet-the-key","title":"RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval","date":"2024-02-28","arxiv_id":"2402.18510","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rnns-are-not-transformers-yet-the-key#ran","syntology_url":"https://syntology.ai/paper/2402.18510","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18510"}},"official":{"repos":["dangxingyu/rnn-icrag"],"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/unsupervised-information-refinement-training","slug":"unsupervised-information-refinement-training","title":"Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation","date":"2024-02-28","arxiv_id":"2402.18150","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-information-refinement-training#ran","syntology_url":"https://syntology.ai/paper/2402.18150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18150"}},"official":{"repos":["xsc1234/info-rag"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-very-long-term-conversational","slug":"evaluating-very-long-term-conversational","title":"Evaluating Very Long-Term Conversational Memory of LLM Agents","date":"2024-02-27","arxiv_id":"2402.17753","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":3,"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 3 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; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/evaluating-very-long-term-conversational#ran","syntology_url":"https://syntology.ai/paper/2402.17753","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17753"}},"official":null}},{"url":"/paper/follow-my-instruction-and-spill-the-beans","slug":"follow-my-instruction-and-spill-the-beans","title":"Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems","date":"2024-02-27","arxiv_id":"2402.17840","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/follow-my-instruction-and-spill-the-beans#ran","syntology_url":"https://syntology.ai/paper/2402.17840","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17840"}},"official":{"repos":["zhentingqi/rag-privacy"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/jmlr-joint-medical-llm-and-retrieval-training","slug":"jmlr-joint-medical-llm-and-retrieval-training","title":"JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability","date":"2024-02-27","arxiv_id":"2402.17887","repositories_listed":1,"syntology":null},{"url":"/paper/rear-a-relevance-aware-retrieval-augmented","slug":"rear-a-relevance-aware-retrieval-augmented","title":"REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering","date":"2024-02-27","arxiv_id":"2402.17497","repositories_listed":1,"syntology":{"n":14,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":14,"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) · 9 unverified","sample_list":"/paper/rear-a-relevance-aware-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2402.17497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17497"}},"official":{"repos":["rucaibox/rear"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/gistembed-guided-in-sample-selection-of","slug":"gistembed-guided-in-sample-selection-of","title":"GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning","date":"2024-02-26","arxiv_id":"2402.16829","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/gistembed-guided-in-sample-selection-of#ran","syntology_url":"https://syntology.ai/paper/2402.16829","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16829"}},"official":{"repos":["avsolatorio/gistembed"],"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/retrieval-augmented-generation-systems","slug":"retrieval-augmented-generation-systems","title":"Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setup","date":"2024-02-26","arxiv_id":"2403.00820","repositories_listed":1,"syntology":null},{"url":"/paper/retrievalqa-assessing-adaptive-retrieval","slug":"retrievalqa-assessing-adaptive-retrieval","title":"RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering","date":"2024-02-26","arxiv_id":"2402.16457","repositories_listed":1,"syntology":null},{"url":"/paper/citation-enhanced-generation-for-llm-based","slug":"citation-enhanced-generation-for-llm-based","title":"Citation-Enhanced Generation for LLM-based Chatbots","date":"2024-02-25","arxiv_id":"2402.16063","repositories_listed":1,"syntology":null},{"url":"/paper/the-good-and-the-bad-exploring-privacy-issues","slug":"the-good-and-the-bad-exploring-privacy-issues","title":"The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)","date":"2024-02-23","arxiv_id":"2402.16893","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/the-good-and-the-bad-exploring-privacy-issues#ran","syntology_url":"https://syntology.ai/paper/2402.16893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.16893"}},"official":{"repos":["phycholosogy/rag-privacy"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/activerag-revealing-the-treasures-of","slug":"activerag-revealing-the-treasures-of","title":"ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents","date":"2024-02-21","arxiv_id":"2402.13547","repositories_listed":1,"syntology":null},{"url":"/paper/arks-active-retrieval-in-knowledge-soup-for","slug":"arks-active-retrieval-in-knowledge-soup-for","title":"EVOR: Evolving Retrieval for Code Generation","date":"2024-02-19","arxiv_id":"2402.12317","repositories_listed":1,"syntology":null},{"url":"/paper/metacognitive-retrieval-augmented-large","slug":"metacognitive-retrieval-augmented-large","title":"Metacognitive Retrieval-Augmented Large Language Models","date":"2024-02-18","arxiv_id":"2402.11626","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/metacognitive-retrieval-augmented-large#ran","syntology_url":"https://syntology.ai/paper/2402.11626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.11626"}},"official":{"repos":["ignorejjj/metarag"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cybermetric-a-benchmark-dataset-for","slug":"cybermetric-a-benchmark-dataset-for","title":"CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge","date":"2024-02-12","arxiv_id":"2402.07688","repositories_listed":1,"syntology":null},{"url":"/paper/wildfiregpt-tailored-large-language-model-for","slug":"wildfiregpt-tailored-large-language-model-for","title":"A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation","date":"2024-02-12","arxiv_id":"2402.07877","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-textbook-question-answering-task","slug":"enhancing-textbook-question-answering-task","title":"Enhancing textual textbook question answering with large language models and retrieval augmented generation","date":"2024-02-05","arxiv_id":"2402.05128","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/enhancing-textbook-question-answering-task#ran","syntology_url":"https://syntology.ai/paper/2402.05128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05128"}},"official":{"repos":["hessaalawwad/plr-tqa"],"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/financial-report-chunking-for-effective","slug":"financial-report-chunking-for-effective","title":"Financial Report Chunking for Effective Retrieval Augmented Generation","date":"2024-02-05","arxiv_id":"2402.05131","repositories_listed":1,"syntology":null},{"url":"/paper/list-aware-reranking-truncation-joint-model","slug":"list-aware-reranking-truncation-joint-model","title":"List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation","date":"2024-02-05","arxiv_id":"2402.02764","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/list-aware-reranking-truncation-joint-model#ran","syntology_url":"https://syntology.ai/paper/2402.02764","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.02764"}},"official":{"repos":["xsc1234/genrt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/how-well-do-llms-cite-relevant-medical","slug":"how-well-do-llms-cite-relevant-medical","title":"How well do LLMs cite relevant medical references? An evaluation framework and analyses","date":"2024-02-03","arxiv_id":"2402.02008","repositories_listed":1,"syntology":null},{"url":"/paper/litllm-a-toolkit-for-scientific-literature","slug":"litllm-a-toolkit-for-scientific-literature","title":"LitLLM: A Toolkit for Scientific Literature Review","date":"2024-02-02","arxiv_id":"2402.01788","repositories_listed":1,"syntology":null},{"url":"/paper/crud-rag-a-comprehensive-chinese-benchmark","slug":"crud-rag-a-comprehensive-chinese-benchmark","title":"CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models","date":"2024-01-30","arxiv_id":"2401.17043","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/crud-rag-a-comprehensive-chinese-benchmark#ran","syntology_url":"https://syntology.ai/paper/2401.17043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17043"}},"official":{"repos":["iaar-shanghai/crud_rag"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/llamp-large-language-model-made-powerful-for","slug":"llamp-large-language-model-made-powerful-for","title":"LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation","date":"2024-01-30","arxiv_id":"2401.17244","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/llamp-large-language-model-made-powerful-for#ran","syntology_url":"https://syntology.ai/paper/2401.17244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17244"}},"official":{"repos":["chiang-yuan/llamp"],"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/improving-medical-reasoning-through-retrieval","slug":"improving-medical-reasoning-through-retrieval","title":"Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models","date":"2024-01-27","arxiv_id":"2401.15269","repositories_listed":1,"syntology":null},{"url":"/paper/from-rag-to-qa-rag-integrating-generative-ai","slug":"from-rag-to-qa-rag-integrating-generative-ai","title":"From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process","date":"2024-01-26","arxiv_id":"2402.01717","repositories_listed":1,"syntology":null},{"url":"/paper/flexibly-scaling-large-language-models","slug":"flexibly-scaling-large-language-models","title":"Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization","date":"2024-01-15","arxiv_id":"2401.07793","repositories_listed":1,"syntology":null},{"url":"/paper/concurrent-brainstorming-hypothesis","slug":"concurrent-brainstorming-hypothesis","title":"Concurrent Brainstorming & Hypothesis Satisfying: An Iterative Framework for Enhanced Retrieval-Augmented Generation (R2CBR3H-SR)","date":"2024-01-03","arxiv_id":"2401.01835","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-survey-of-hallucination","slug":"a-comprehensive-survey-of-hallucination","title":"A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models","date":"2024-01-02","arxiv_id":"2401.01313","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/a-comprehensive-survey-of-hallucination#ran","syntology_url":"https://syntology.ai/paper/2401.01313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.01313"}},"official":null}},{"url":"/paper/advancing-ttp-analysis-harnessing-the-power","slug":"advancing-ttp-analysis-harnessing-the-power","title":"Advancing TTP Analysis: Harnessing the Power of Large Language Models with Retrieval Augmented Generation","date":"2023-12-30","arxiv_id":"2401.00280","repositories_listed":1,"syntology":null},{"url":"/paper/think-and-retrieval-a-hypothesis-knowledge","slug":"think-and-retrieval-a-hypothesis-knowledge","title":"HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses","date":"2023-12-26","arxiv_id":"2312.15883","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-neural-graph-databases","slug":"privacy-preserving-neural-graph-databases","title":"Privacy-Preserved Neural Graph Databases","date":"2023-12-25","arxiv_id":"2312.15591","repositories_listed":1,"syntology":null},{"url":"/paper/readme-bridging-medical-jargon-and-lay","slug":"readme-bridging-medical-jargon-and-lay","title":"README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP","date":"2023-12-24","arxiv_id":"2312.15561","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-decoding-reduces-hallucination","slug":"context-aware-decoding-reduces-hallucination","title":"Context-aware Decoding Reduces Hallucination in Query-focused Summarization","date":"2023-12-21","arxiv_id":"2312.14335","repositories_listed":1,"syntology":null},{"url":"/paper/nomiracl-knowing-when-you-don-t-know-for","slug":"nomiracl-knowing-when-you-don-t-know-for","title":"\"Knowing When You Don't Know\": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation","date":"2023-12-18","arxiv_id":"2312.11361","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/nomiracl-knowing-when-you-don-t-know-for#ran","syntology_url":"https://syntology.ai/paper/2312.11361","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.11361"}},"official":{"repos":["project-miracl/nomiracl"],"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/right-retrieval-augmented-generation-for","slug":"right-retrieval-augmented-generation-for","title":"RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation","date":"2023-12-16","arxiv_id":"2312.10466","repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-retrieval-augmented-generation-rag","slug":"harnessing-retrieval-augmented-generation-rag","title":"Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps","date":"2023-12-12","arxiv_id":"2312.07796","repositories_listed":1,"syntology":null},{"url":"/paper/a-glitch-in-the-matrix-locating-and-detecting","slug":"a-glitch-in-the-matrix-locating-and-detecting","title":"A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia","date":"2023-12-04","arxiv_id":"2312.02073","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/a-glitch-in-the-matrix-locating-and-detecting#ran","syntology_url":"https://syntology.ai/paper/2312.02073","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.02073"}},"official":{"repos":["epfl-dlab/llm-grounding-analysis"],"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/biomedical-knowledge-graph-enhanced-prompt","slug":"biomedical-knowledge-graph-enhanced-prompt","title":"Biomedical knowledge graph-optimized prompt generation for large language models","date":"2023-11-29","arxiv_id":"2311.17330","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/biomedical-knowledge-graph-enhanced-prompt#ran","syntology_url":"https://syntology.ai/paper/2311.17330","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.17330"}},"official":{"repos":["BaranziniLab/KG_RAG"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/speak-like-a-native-prompting-large-language","slug":"speak-like-a-native-prompting-large-language","title":"AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations","date":"2023-11-22","arxiv_id":"2311.13538","repositories_listed":1,"syntology":null},{"url":"/paper/ares-an-automated-evaluation-framework-for","slug":"ares-an-automated-evaluation-framework-for","title":"ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems","date":"2023-11-16","arxiv_id":"2311.09476","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ares-an-automated-evaluation-framework-for#ran","syntology_url":"https://syntology.ai/paper/2311.09476","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09476"}},"official":{"repos":["stanford-futuredata/ares"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ever-mitigating-hallucination-in-large","slug":"ever-mitigating-hallucination-in-large","title":"Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification","date":"2023-11-15","arxiv_id":"2311.09114","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-evaluation-of-gpt-4v-on","slug":"a-comprehensive-evaluation-of-gpt-4v-on","title":"A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering","date":"2023-11-13","arxiv_id":"2311.07536","repositories_listed":1,"syntology":null},{"url":"/paper/chata-towards-an-intelligent-question-answer","slug":"chata-towards-an-intelligent-question-answer","title":"AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs","date":"2023-11-05","arxiv_id":"2311.02775","repositories_listed":1,"syntology":null},{"url":"/paper/disc-finllm-a-chinese-financial-large","slug":"disc-finllm-a-chinese-financial-large","title":"DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning","date":"2023-10-23","arxiv_id":"2310.15205","repositories_listed":1,"syntology":null},{"url":"/paper/chainpoll-a-high-efficacy-method-for-llm","slug":"chainpoll-a-high-efficacy-method-for-llm","title":"Chainpoll: A high efficacy method for LLM hallucination detection","date":"2023-10-22","arxiv_id":"2310.18344","repositories_listed":1,"syntology":null},{"url":"/paper/qilin-med-multi-stage-knowledge-injection","slug":"qilin-med-multi-stage-knowledge-injection","title":"Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model","date":"2023-10-13","arxiv_id":"2310.09089","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/qilin-med-multi-stage-knowledge-injection#ran","syntology_url":"https://syntology.ai/paper/2310.09089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09089"}},"official":{"repos":["williamliujl/Qilin-Med"],"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","unlocated"]}}},{"url":"/paper/gentkg-generative-forecasting-on-temporal","slug":"gentkg-generative-forecasting-on-temporal","title":"GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models","date":"2023-10-11","arxiv_id":"2310.07793","repositories_listed":1,"syntology":null},{"url":"/paper/glitter-or-gold-deriving-structured-insights","slug":"glitter-or-gold-deriving-structured-insights","title":"Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models","date":"2023-10-09","arxiv_id":"2310.05628","repositories_listed":1,"syntology":null},{"url":"/paper/llm4vv-developing-llm-driven-testsuite-for","slug":"llm4vv-developing-llm-driven-testsuite-for","title":"LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation","date":"2023-10-08","arxiv_id":"2310.04963","repositories_listed":1,"syntology":null},{"url":"/paper/attention-sorting-combats-recency-bias-in","slug":"attention-sorting-combats-recency-bias-in","title":"Attention Sorting Combats Recency Bias In Long Context Language Models","date":"2023-09-28","arxiv_id":"2310.01427","repositories_listed":1,"syntology":null},{"url":"/paper/graph-neural-prompting-with-large-language","slug":"graph-neural-prompting-with-large-language","title":"Graph Neural Prompting with Large Language Models","date":"2023-09-27","arxiv_id":"2309.15427","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-large-language-models-in","slug":"benchmarking-large-language-models-in","title":"Benchmarking Large Language Models in Retrieval-Augmented Generation","date":"2023-09-04","arxiv_id":"2309.01431","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/benchmarking-large-language-models-in#ran","syntology_url":"https://syntology.ai/paper/2309.01431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01431"}},"official":{"repos":["chen700564/RGB"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-parameter-efficient-fine-tuning","slug":"exploring-parameter-efficient-fine-tuning","title":"Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models","date":"2023-08-21","arxiv_id":"2308.10462","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/exploring-parameter-efficient-fine-tuning#ran","syntology_url":"https://syntology.ai/paper/2308.10462","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10462"}},"official":{"repos":["martin-wey/peft-llm-code"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/trac-trustworthy-retrieval-augmented-chatbot","slug":"trac-trustworthy-retrieval-augmented-chatbot","title":"TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction","date":"2023-07-07","arxiv_id":"2307.04642","repositories_listed":1,"syntology":null},{"url":"/paper/unipoll-a-unified-social-media-poll","slug":"unipoll-a-unified-social-media-poll","title":"UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization","date":"2023-06-12","arxiv_id":"2306.06851","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/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/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/you-truly-understand-what-i-need-intellectual","slug":"you-truly-understand-what-i-need-intellectual","title":"You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona","date":"2023-01-06","arxiv_id":"2301.02401","repositories_listed":1,"syntology":null},{"url":"/paper/core-a-retrieve-then-edit-framework-for","slug":"core-a-retrieve-then-edit-framework-for","title":"CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation","date":"2022-10-10","arxiv_id":"2210.04873","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-domain-adaptation-of-retrieval","slug":"improving-the-domain-adaptation-of-retrieval","title":"Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering","date":"2022-10-06","arxiv_id":"2210.02627","repositories_listed":1,"syntology":null},{"url":"/paper/evidentiality-guided-generation-for-knowledge","slug":"evidentiality-guided-generation-for-knowledge","title":"Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks","date":"2021-12-16","arxiv_id":"2112.08688","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":7,"phrase":"10 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; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/evidentiality-guided-generation-for-knowledge#ran","syntology_url":"https://syntology.ai/paper/2112.08688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.08688"}},"official":{"repos":["akariasai/evidentiality_qa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-learning-of-flowchart-grounded","slug":"end-to-end-learning-of-flowchart-grounded","title":"End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs","date":"2021-09-15","arxiv_id":"2109.07263","repositories_listed":1,"syntology":{"n":16,"n_ran":9,"n_constructed":0,"n_ran_checked":0,"n_instrument":9,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":16,"phrase":"9 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; 9 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/end-to-end-learning-of-flowchart-grounded#ran","syntology_url":"https://syntology.ai/paper/2109.07263","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07263"}},"official":{"repos":["dair-iitd/flonet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/memory-and-knowledge-augmented-language","slug":"memory-and-knowledge-augmented-language","title":"Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories","date":"2021-09-08","arxiv_id":"2109.03754","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-retrieval-augmented-generation-from","slug":"efficient-retrieval-augmented-generation-from","title":"Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog","date":"2021-02-09","arxiv_id":"2102.04643","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-code-summarization-via-multi","slug":"automatic-code-summarization-via-multi","title":"Retrieval-Augmented Generation for Code Summarization via Hybrid GNN","date":"2020-06-09","arxiv_id":"2006.05405","repositories_listed":1,"syntology":null},{"url":null,"slug":"a-survey-of-context-engineering-for-large","title":"A Survey of Context Engineering for Large Language Models","date":"2025-07-17","arxiv_id":"2507.13334","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-aware-search-and-retrieval-over","title":"Context-Aware Search and Retrieval Over Erasure Channels","date":"2025-07-16","arxiv_id":"2507.11894","repositories_listed":0,"syntology":null},{"url":null,"slug":"cli-rag-a-retrieval-augmented-framework-for","title":"CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs","date":"2025-07-09","arxiv_id":"2507.06715","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-retrieval-augmented-framework-for","title":"Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation","date":"2025-07-09","arxiv_id":"2507.07307","repositories_listed":0,"syntology":null},{"url":null,"slug":"flippi-end-to-end-genai-assistant-for-e","title":"Flippi: End To End GenAI Assistant for E-Commerce","date":"2025-07-08","arxiv_id":"2507.05788","repositories_listed":0,"syntology":null},{"url":null,"slug":"sara-selective-and-adaptive-retrieval","title":"SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression","date":"2025-07-08","arxiv_id":"2507.05633","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-memory-in-llm-agents-via","title":"Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions","date":"2025-07-07","arxiv_id":"2507.05257","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-vaxguide-an-agentic-rag-based-llm-for","title":"AI-VaxGuide: An Agentic RAG-Based LLM for Vaccination Decisions","date":"2025-07-04","arxiv_id":"2507.03493","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyberrag-an-agentic-rag-cyber-attack","title":"CyberRAG: An agentic RAG cyber attack classification and reporting tool","date":"2025-07-03","arxiv_id":"2507.02424","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-protocol-engineering-a-new-paradigm","title":"Knowledge Protocol Engineering: A New Paradigm for AI in Domain-Specific Knowledge Work","date":"2025-07-03","arxiv_id":"2507.02760","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-augmented-finetuning-matters-in","title":"Knowledge Augmented Finetuning Matters in both RAG and Agent Based Dialog Systems","date":"2025-06-28","arxiv_id":"2506.22852","repositories_listed":0,"syntology":null},{"url":null,"slug":"arag-agentic-retrieval-augmented-generation","title":"ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation","date":"2025-06-27","arxiv_id":"2506.21931","repositories_listed":0,"syntology":null},{"url":null,"slug":"comrag-retrieval-augmented-generation-with","title":"ComRAG: Retrieval-Augmented Generation with Dynamic Vector Stores for Real-time Community Question Answering in Industry","date":"2025-06-26","arxiv_id":"2506.21098","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-acing-chartered","title":"Large Language Models Acing Chartered Accountancy","date":"2025-06-26","arxiv_id":"2506.21031","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-llm-assisted-query-understanding","title":"Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation","date":"2025-06-26","arxiv_id":"2506.21384","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-assistants-to-enhance-and-exploit-the","title":"AI Assistants to Enhance and Exploit the PETSc Knowledge Base","date":"2025-06-25","arxiv_id":"2506.20608","repositories_listed":0,"syntology":null},{"url":null,"slug":"engineering-rag-systems-for-real-world","title":"Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation","date":"2025-06-25","arxiv_id":"2506.20869","repositories_listed":0,"syntology":null},{"url":null,"slug":"enterprise-large-language-model-evaluation","title":"Enterprise Large Language Model Evaluation Benchmark","date":"2025-06-25","arxiv_id":"2506.20274","repositories_listed":0,"syntology":null},{"url":null,"slug":"multifinrag-an-optimized-multimodal-retrieval","title":"MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering","date":"2025-06-25","arxiv_id":"2506.20821","repositories_listed":0,"syntology":null},{"url":null,"slug":"sv-llm-an-agentic-approach-for-soc-security","title":"SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models","date":"2025-06-25","arxiv_id":"2506.20415","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-energy-efficient-local-retrieval","title":"Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks","date":"2025-06-24","arxiv_id":"2506.20009","repositories_listed":0,"syntology":null},{"url":null,"slug":"controlled-retrieval-augmented-context","title":"Controlled Retrieval-augmented Context Evaluation for Long-form RAG","date":"2025-06-24","arxiv_id":"2506.20051","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialogic-pedagogy-for-large-language-models","title":"Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning","date":"2025-06-24","arxiv_id":"2506.19484","repositories_listed":0,"syntology":null}],"record_sha256":"20c7b16f6beef889787457ba6a5bc42708d658e4a67b92ec101cf3236974d463","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}