{"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":"/method/rag/papers/13","list_of":"/method/rag","method":"RAG","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":13,"pages_in_order":13,"rows_per_page":100,"rows":[1201,1286],"of":1286,"counts":{"archive_papers_tagged":1286,"with_a_code_link":488,"where_syntology_ran_a_sample":144,"not_listed_spam_title":0,"listed":1286,"listed_where_code_ran":144,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":116,"every_run_a_failure_of_syntologys_instrument":28,"listed_with_a_run_with_no_instrument_failure":116,"listed_every_run_a_failure_of_syntologys_instrument":28,"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":"/method/rag","prev":"/method/rag/papers/12","next":null,"papers":[{"paper":null,"slug":"gendec-a-robust-generative-question","title":"GenDec: A robust generative Question-decomposition method for Multi-hop reasoning","date":"2024-02-17","arxiv_id":"2402.11166","n_code_links":0,"syntology":null},{"paper":"/paper/in-search-of-needles-in-a-10m-haystack","slug":"in-search-of-needles-in-a-10m-haystack","title":"In Search of Needles in a 11M Haystack: Recurrent Memory Finds What LLMs Miss","date":"2024-02-16","arxiv_id":"2402.10790","n_code_links":2,"syntology":null},{"paper":"/paper/unsupervised-llm-adaptation-for-question","slug":"unsupervised-llm-adaptation-for-question","title":"Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction","date":"2024-02-16","arxiv_id":"2402.12170","n_code_links":1,"syntology":null},{"paper":null,"slug":"grounding-language-model-with-chunking-free","title":"Grounding Language Model with Chunking-Free In-Context Retrieval","date":"2024-02-15","arxiv_id":"2402.09760","n_code_links":0,"syntology":null},{"paper":"/paper/g-retriever-retrieval-augmented-generation","slug":"g-retriever-retrieval-augmented-generation","title":"G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering","date":"2024-02-12","arxiv_id":"2402.07630","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"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","official":{"repos":["xiaoxinhe/g-retriever"],"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"]}}},{"paper":"/paper/poisonedrag-knowledge-poisoning-attacks-to","slug":"poisonedrag-knowledge-poisoning-attacks-to","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","date":"2024-02-12","arxiv_id":"2402.07867","n_code_links":2,"syntology":{"ran":12,"of":17,"n_ran_checked":11,"n_instrument":1,"unverified":5,"pointer_only":7,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["sleeepeer/poisonedrag"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"t-rag-lessons-from-the-llm-trenches","title":"T-RAG: Lessons from the LLM Trenches","date":"2024-02-12","arxiv_id":"2402.07483","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-perturbation-in-retrieval-augmented","title":"Prompt Perturbation in Retrieval-Augmented Generation based Large Language Models","date":"2024-02-11","arxiv_id":"2402.07179","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-retrieval-processes-for-language","title":"Enhancing Retrieval Processes for Language Generation with Augmented Queries","date":"2024-02-06","arxiv_id":"2402.16874","n_code_links":0,"syntology":null},{"paper":"/paper/c-rag-certified-generation-risks-for","slug":"c-rag-certified-generation-risks-for","title":"C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models","date":"2024-02-05","arxiv_id":"2402.03181","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["kangmintong/c-rag"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/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","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","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":[]}}},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-lingual-malaysian-embedding-leveraging","title":"Multi-Lingual Malaysian Embedding: Leveraging Large Language Models for Semantic Representations","date":"2024-02-05","arxiv_id":"2402.03053","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-assessment-of-tutoring-practices","title":"Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation","date":"2024-02-04","arxiv_id":"2402.14594","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-end-to-end-spoken-dialog","title":"Retrieval Augmented End-to-End Spoken Dialog Models","date":"2024-02-02","arxiv_id":"2402.01828","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-unified-language-model-for","title":"CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks","date":"2024-02-02","arxiv_id":"2402.01176","n_code_links":0,"syntology":null},{"paper":null,"slug":"hiqa-a-hierarchical-contextual-augmentation","title":"HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA","date":"2024-02-01","arxiv_id":"2402.01767","n_code_links":0,"syntology":null},{"paper":null,"slug":"rag-fusion-a-new-take-on-retrieval-augmented","title":"RAG-Fusion: a New Take on Retrieval-Augmented Generation","date":"2024-01-31","arxiv_id":"2402.03367","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"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","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"]}}},{"paper":null,"slug":"large-multi-modal-models-lmms-as-universal","title":"Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems","date":"2024-01-30","arxiv_id":"2402.01748","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-and-testing-of-a-novel-large","title":"Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties","date":"2024-01-29","arxiv_id":"2402.01741","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-and-testing-of-retrieval","title":"Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report","date":"2024-01-29","arxiv_id":"2402.01733","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-large-language-model-performance-to","title":"Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately","date":"2024-01-27","arxiv_id":"2402.01722","n_code_links":0,"syntology":null},{"paper":"/paper/multihop-rag-benchmarking-retrieval-augmented","slug":"multihop-rag-benchmarking-retrieval-augmented","title":"MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries","date":"2024-01-27","arxiv_id":"2401.15391","n_code_links":2,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yixuantt/MultiHop-RAG"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":"/paper/the-power-of-noise-redefining-retrieval-for","slug":"the-power-of-noise-redefining-retrieval-for","title":"The Power of Noise: Redefining Retrieval for RAG Systems","date":"2024-01-26","arxiv_id":"2401.14887","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["florin-git/The-Power-of-Noise"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"revolutionizing-retrieval-augmented","title":"Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition","date":"2024-01-23","arxiv_id":"2401.12599","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-rag-pioneering-vector-embedding-free","title":"Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine","date":"2024-01-20","arxiv_id":"2401.11246","n_code_links":0,"syntology":null},{"paper":"/paper/chatqa-building-gpt-4-level-conversational-qa","slug":"chatqa-building-gpt-4-level-conversational-qa","title":"ChatQA: Surpassing GPT-4 on Conversational QA and RAG","date":"2024-01-18","arxiv_id":"2401.10225","n_code_links":0,"syntology":null},{"paper":null,"slug":"rag-vs-fine-tuning-pipelines-tradeoffs-and-a","title":"RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture","date":"2024-01-16","arxiv_id":"2401.08406","n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-database-while-computationally","title":"Graph database while computationally efficient filters out quickly the ESG integrated equities in investment management","date":"2024-01-15","arxiv_id":"2401.07483","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-chronicles-of-rag-the-retriever-the-chunk","title":"The Chronicles of RAG: The Retriever, the Chunk and the Generator","date":"2024-01-15","arxiv_id":"2401.07883","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridging-the-preference-gap-between","title":"Bridging the Preference Gap between Retrievers and LLMs","date":"2024-01-13","arxiv_id":"2401.06954","n_code_links":0,"syntology":null},{"paper":null,"slug":"reinforcement-learning-for-optimizing-rag-for","title":"Reinforcement Learning for Optimizing RAG for Domain Chatbots","date":"2024-01-10","arxiv_id":"2401.06800","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-large-language-models-are","title":"Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models","date":"2024-01-05","arxiv_id":"2401.02851","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-extraction-contextualising-tabular","title":"Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models","date":"2024-01-04","arxiv_id":"2401.02333","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-multilingual-information-retrieval","title":"Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise","date":"2024-01-03","arxiv_id":"2401.01511","n_code_links":0,"syntology":null},{"paper":"/paper/ragtruth-a-hallucination-corpus-for","slug":"ragtruth-a-hallucination-corpus-for","title":"RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models","date":"2023-12-31","arxiv_id":"2401.00396","n_code_links":3,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"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","official":{"repos":["particlemedia/ragtruth"],"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"]}}},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":"/paper/lookahead-an-inference-acceleration-framework","slug":"lookahead-an-inference-acceleration-framework","title":"Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy","date":"2023-12-20","arxiv_id":"2312.12728","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["alipay/PainlessInferenceAcceleration"],"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"]}}},{"paper":null,"slug":"efficient-title-reranker-for-fast-and","title":"Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP","date":"2023-12-19","arxiv_id":"2312.12430","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":5,"of":6,"n_ran_checked":5,"n_instrument":0,"unverified":1,"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","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"]}}},{"paper":"/paper/retrieval-augmented-generation-for-large","slug":"retrieval-augmented-generation-for-large","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","date":"2023-12-18","arxiv_id":"2312.10997","n_code_links":4,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"fine-tuning-or-retrieval-comparing-knowledge","title":"Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs","date":"2023-12-10","arxiv_id":"2312.05934","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-tuning-for-retrieval-augmented","title":"Context Tuning for Retrieval Augmented Generation","date":"2023-12-09","arxiv_id":"2312.05708","n_code_links":0,"syntology":null},{"paper":null,"slug":"paperqa-retrieval-augmented-generative-agent","title":"PaperQA: Retrieval-Augmented Generative Agent for Scientific Research","date":"2023-12-08","arxiv_id":"2312.07559","n_code_links":0,"syntology":null},{"paper":"/paper/fortify-the-shortest-stave-in-attention","slug":"fortify-the-shortest-stave-in-attention","title":"Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use","date":"2023-12-07","arxiv_id":"2312.04455","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["fiorina1212/attention-buckets"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"iag-induction-augmented-generation-framework","title":"IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions","date":"2023-11-30","arxiv_id":"2311.18397","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","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":[]}}},{"paper":null,"slug":"minimizing-factual-inconsistency-and","title":"Minimizing Factual Inconsistency and Hallucination in Large Language Models","date":"2023-11-23","arxiv_id":"2311.13878","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":{"ran":13,"of":15,"n_ran_checked":13,"n_instrument":0,"unverified":2,"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","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"]}}},{"paper":null,"slug":"towards-an-automatic-ai-agent-for-reaction","title":"Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis","date":"2023-11-16","arxiv_id":"2311.10776","n_code_links":0,"syntology":null},{"paper":null,"slug":"establishing-performance-baselines-in-fine","title":"Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users","date":"2023-11-10","arxiv_id":"2311.05903","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-llm-intelligence-with-arm-rag","title":"Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation","date":"2023-11-07","arxiv_id":"2311.04177","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"gar-meets-rag-paradigm-for-zero-shot","title":"GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval","date":"2023-10-31","arxiv_id":"2310.20158","n_code_links":0,"syntology":null},{"paper":null,"slug":"fabula-intelligence-report-generation-using","title":"FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction","date":"2023-10-20","arxiv_id":"2310.13848","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-4-as-an-agronomist-assistant-answering","title":"GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models","date":"2023-10-10","arxiv_id":"2310.06225","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-to-improve","slug":"retrieval-augmented-generation-to-improve","title":"Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference","date":"2023-10-04","arxiv_id":"2310.03184","n_code_links":2,"syntology":{"ran":12,"of":20,"n_ran_checked":12,"n_instrument":0,"unverified":8,"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) · 8 unverified","official":{"repos":["digitalharborfoundation/rag-for-math-qa"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"intuitive-or-dependent-investigating-llms","title":"Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts","date":"2023-09-29","arxiv_id":"2309.17415","n_code_links":0,"syntology":null},{"paper":null,"slug":"mededit-model-editing-for-medical-question","title":"MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering","date":"2023-09-27","arxiv_id":"2309.16035","n_code_links":0,"syntology":null},{"paper":"/paper/ragas-automated-evaluation-of-retrieval","slug":"ragas-automated-evaluation-of-retrieval","title":"RAGAS: Automated Evaluation of Retrieval Augmented Generation","date":"2023-09-26","arxiv_id":"2309.15217","n_code_links":3,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":null}},{"paper":"/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","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"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","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"]}}},{"paper":null,"slug":"a-study-on-the-implementation-of-generative","title":"A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture","date":"2023-09-03","arxiv_id":"2309.01105","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-and","slug":"retrieval-augmented-generation-and","title":"Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education","date":"2023-08-01","arxiv_id":"2308.00479","n_code_links":2,"syntology":null},{"paper":null,"slug":"prompt-generate-train-pgt-a-framework-for-few","title":"Prompt Generate Train (PGT): Few-shot Domain Adaption of Retrieval Augmented Generation Models for Open Book Question-Answering","date":"2023-07-12","arxiv_id":"2307.05915","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":"/paper/deepmerge-deep-learning-based-region-merging","slug":"deepmerge-deep-learning-based-region-merging","title":"DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation","date":"2023-05-31","arxiv_id":"2305.19787","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatio-temporal-driven-attention-graph-neural","title":"Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA)","date":"2023-03-25","arxiv_id":"2303.14322","n_code_links":0,"syntology":null},{"paper":"/paper/robust-affine-feature-matching-via-quadratic","slug":"robust-affine-feature-matching-via-quadratic","title":"Robust affine point matching via quadratic assignment on Grassmannians","date":"2023-03-05","arxiv_id":"2303.02698","n_code_links":3,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"murag-multimodal-retrieval-augmented","title":"MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text","date":"2022-10-06","arxiv_id":"2210.02928","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-action-governor-for-uncertain","title":"Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learning","date":"2022-07-17","arxiv_id":"2207.08240","n_code_links":0,"syntology":null},{"paper":"/paper/re2g-retrieve-rerank-generate-2","slug":"re2g-retrieve-rerank-generate-2","title":"Re2G: Retrieve, Rerank, Generate","date":"2022-07-13","arxiv_id":"2207.06300","n_code_links":1,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ibm/kgi-slot-filling"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"resource-aware-distributed-submodular","title":"Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making","date":"2022-04-15","arxiv_id":"2204.07520","n_code_links":0,"syntology":null},{"paper":null,"slug":"re2g-retrieve-rerank-generate","title":"Re2G: Retrieve, Rerank, Generate","date":"2022-01-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/continual-stereo-matching-of-continuous","slug":"continual-stereo-matching-of-continuous","title":"Continual Stereo Matching of Continuous Driving Scenes With Growing Architecture","date":"2022-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/variational-learning-for-unsupervised-1","slug":"variational-learning-for-unsupervised-1","title":"Variational Learning for Unsupervised Knowledge Grounded Dialogs","date":"2021-11-23","arxiv_id":"2112.00653","n_code_links":1,"syntology":null},{"paper":"/paper/fine-tune-the-entire-rag-architecture","slug":"fine-tune-the-entire-rag-architecture","title":"Fine-tune the Entire RAG Architecture (including DPR retriever) for Question-Answering","date":"2021-06-22","arxiv_id":"2106.11517","n_code_links":2,"syntology":null},{"paper":"/paper/end-to-end-multihop-retrieval-for","slug":"end-to-end-multihop-retrieval-for","title":"Iterative Hierarchical Attention for Answering Complex Questions over Long Documents","date":"2021-06-01","arxiv_id":"2106.00200","n_code_links":0,"syntology":null},{"paper":"/paper/zero-shot-slot-filling-with-dpr-and-rag","slug":"zero-shot-slot-filling-with-dpr-and-rag","title":"Zero-shot Slot Filling with DPR and RAG","date":"2021-04-17","arxiv_id":"2104.08610","n_code_links":2,"syntology":null},{"paper":"/paper/end-to-end-training-of-neural-retrievers-for","slug":"end-to-end-training-of-neural-retrievers-for","title":"End-to-End Training of Neural Retrievers for Open-Domain Question Answering","date":"2021-01-02","arxiv_id":"2101.00408","n_code_links":2,"syntology":null},{"paper":"/paper/two-stage-single-image-reflection-removal","slug":"two-stage-single-image-reflection-removal","title":"Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance","date":"2020-12-02","arxiv_id":"2012.00945","n_code_links":1,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-for-knowledge","slug":"retrieval-augmented-generation-for-knowledge","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","date":"2020-05-22","arxiv_id":"2005.11401","n_code_links":18,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}}],"record_sha256":"72c2b24ceb4c755c07cd19ffec3ac2bfaba2195d4b864259f1358b00df05d2e3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}