{"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/rag/papers/19","list_of":"/task/rag","task":"RAG","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":19,"pages_in_order":22,"rows_per_page":100,"rows":[1801,1900],"of":2111,"counts":{"archive_papers_tagged":2111,"with_a_code_link":758,"where_syntology_ran_a_sample":211,"not_listed_spam_title":0,"listed":2111,"listed_where_code_ran":211,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":170,"every_run_a_failure_of_syntologys_instrument":41,"listed_with_a_run_with_no_instrument_failure":170,"listed_every_run_a_failure_of_syntologys_instrument":41,"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/rag","prev":"/task/rag/papers/18","next":"/task/rag/papers/20","papers":[{"url":null,"slug":"lawluo-a-chinese-law-firm-co-run-by-llm","title":"LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation","date":"2024-07-23","arxiv_id":"2407.16252","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-or-long","title":"Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach","date":"2024-07-23","arxiv_id":"2407.16833","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-comparison-of-video-frame","title":"An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval","date":"2024-07-22","arxiv_id":"2408.03340","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-retrieval-augmented","title":"An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought","date":"2024-07-22","arxiv_id":"2407.15569","repositories_listed":0,"syntology":null},{"url":null,"slug":"customized-retrieval-augmented-generation-and","title":"Customized Retrieval Augmented Generation and Benchmarking for EDA Tool Documentation QA","date":"2024-07-22","arxiv_id":"2407.15353","repositories_listed":0,"syntology":null},{"url":null,"slug":"decoding-bacnet-packets-a-large-language","title":"Decoding BACnet Packets: A Large Language Model Approach for Packet Interpretation","date":"2024-07-22","arxiv_id":"2407.15428","repositories_listed":0,"syntology":null},{"url":null,"slug":"kapqa-knowledge-augmented-product-question","title":"KaPQA: Knowledge-Augmented Product Question-Answering","date":"2024-07-22","arxiv_id":"2407.16073","repositories_listed":0,"syntology":null},{"url":null,"slug":"morse-bridging-the-gap-in-cybersecurity","title":"MoRSE: Bridging the Gap in Cybersecurity Expertise with Retrieval Augmented Generation","date":"2024-07-22","arxiv_id":"2407.15748","repositories_listed":0,"syntology":null},{"url":null,"slug":"nv-retriever-improving-text-embedding-models","title":"NV-Retriever: Improving text embedding models with effective hard-negative mining","date":"2024-07-22","arxiv_id":"2407.15831","repositories_listed":0,"syntology":null},{"url":null,"slug":"autovcoder-a-systematic-framework-for","title":"AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs","date":"2024-07-21","arxiv_id":"2407.18333","repositories_listed":0,"syntology":null},{"url":null,"slug":"fact-aware-multimodal-retrieval-augmentation","title":"Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation","date":"2024-07-21","arxiv_id":"2407.15268","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-00798","title":"Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base","date":"2024-07-20","arxiv_id":"2408.00798","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-privacy-of-cross-attention-with","title":"Differential Privacy of Cross-Attention with Provable Guarantee","date":"2024-07-20","arxiv_id":"2407.14717","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-integrated","title":"Retrieval Augmented Generation Integrated Large Language Models in Smart Contract Vulnerability Detection","date":"2024-07-20","arxiv_id":"2407.14838","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-databases-improve-success-in","title":"Adversarial Databases Improve Success in Retrieval-based Large Language Models","date":"2024-07-19","arxiv_id":"2407.14609","repositories_listed":0,"syntology":null},{"url":null,"slug":"chatqa-2-bridging-the-gap-to-proprietary-llms","title":"ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities","date":"2024-07-19","arxiv_id":"2407.14482","repositories_listed":0,"syntology":null},{"url":null,"slug":"black-box-opinion-manipulation-attacks-to","title":"Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models","date":"2024-07-18","arxiv_id":"2407.13757","repositories_listed":0,"syntology":null},{"url":null,"slug":"pragyan-connecting-the-dots-in-tweets","title":"PRAGyan -- Connecting the Dots in Tweets","date":"2024-07-18","arxiv_id":"2407.13909","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-for-natural","title":"Retrieval-Augmented Generation for Natural Language Processing: A Survey","date":"2024-07-18","arxiv_id":"2407.13193","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieve-summarize-plan-advancing-multi-hop","title":"Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach","date":"2024-07-18","arxiv_id":"2407.13101","repositories_listed":0,"syntology":null},{"url":null,"slug":"echosight-advancing-visual-language-models","title":"EchoSight: Advancing Visual-Language Models with Wiki Knowledge","date":"2024-07-17","arxiv_id":"2407.12735","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-biomedical-hypothesis-generation","title":"Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models","date":"2024-07-17","arxiv_id":"2407.12888","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-query-generation-for-enhanced","title":"Optimizing Query Generation for Enhanced Document Retrieval in RAG","date":"2024-07-17","arxiv_id":"2407.12325","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-evaluation-of-large-language-2","title":"A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting","date":"2024-07-16","arxiv_id":"2407.11638","repositories_listed":0,"syntology":null},{"url":null,"slug":"mindful-rag-a-study-of-points-of-failure-in","title":"Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation","date":"2024-07-16","arxiv_id":"2407.12216","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-rag-metrics-for-question","title":"Evaluation of RAG Metrics for Question Answering in the Telecom Domain","date":"2024-07-15","arxiv_id":"2407.12873","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-and-prompt-optimization-two-great","title":"Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together","date":"2024-07-15","arxiv_id":"2407.10930","repositories_listed":0,"syntology":null},{"url":null,"slug":"gensco-can-question-decomposition-based","title":"GenSco: Can Question Decomposition based Passage Alignment improve Question Answering?","date":"2024-07-14","arxiv_id":"2407.10245","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-ai-tutors-in-a-programming-course","title":"Integrating AI Tutors in a Programming Course","date":"2024-07-14","arxiv_id":"2407.15718","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-level-clinical-entity-and-relation","title":"Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation","date":"2024-07-13","arxiv_id":"2407.10021","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-analysis-of-in-context-linear","title":"Fine-grained Analysis of In-context Linear Estimation: Data, Architecture, and Beyond","date":"2024-07-13","arxiv_id":"2407.10005","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-embeddings-for-efficient-answer","title":"Context Embeddings for Efficient Answer Generation in RAG","date":"2024-07-12","arxiv_id":"2407.09252","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-llms-as-voting-assistants-via","title":"Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024","date":"2024-07-11","arxiv_id":"2407.08495","repositories_listed":0,"syntology":null},{"url":null,"slug":"lynx-an-open-source-hallucination-evaluation","title":"Lynx: An Open Source Hallucination Evaluation Model","date":"2024-07-11","arxiv_id":"2407.08488","repositories_listed":0,"syntology":null},{"url":null,"slug":"speculative-rag-enhancing-retrieval-augmented","title":"Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting","date":"2024-07-11","arxiv_id":"2407.08223","repositories_listed":0,"syntology":null},{"url":null,"slug":"facts-about-building-retrieval-augmented","title":"FACTS About Building Retrieval Augmented Generation-based Chatbots","date":"2024-07-10","arxiv_id":"2407.07858","repositories_listed":0,"syntology":null},{"url":null,"slug":"rag-vs-long-context-examining-frontier-large","title":"Examining Long-Context Large Language Models for Environmental Review Document Comprehension","date":"2024-07-10","arxiv_id":"2407.07321","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-architecture-for-enterprise-large","title":"A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts","date":"2024-07-09","arxiv_id":"2407.06718","repositories_listed":0,"syntology":null},{"url":null,"slug":"faux-polyglot-a-study-on-information","title":"Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models","date":"2024-07-07","arxiv_id":"2407.05502","repositories_listed":0,"syntology":null},{"url":null,"slug":"ramo-retrieval-augmented-generation-for","title":"RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendations","date":"2024-07-06","arxiv_id":"2407.04925","repositories_listed":0,"syntology":null},{"url":null,"slug":"vortex-under-ripplet-an-empirical-study-of","title":"Are LLMs Correctly Integrated into Software Systems?","date":"2024-07-06","arxiv_id":"2407.05138","repositories_listed":0,"syntology":null},{"url":null,"slug":"eventchat-implementation-and-user-centric","title":"EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context","date":"2024-07-05","arxiv_id":"2407.04472","repositories_listed":0,"syntology":null},{"url":null,"slug":"gpt-vs-retro-exploring-the-intersection-of","title":"GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning","date":"2024-07-05","arxiv_id":"2407.04528","repositories_listed":0,"syntology":null},{"url":"/paper/rethinking-visual-prompting-for-multimodal","slug":"rethinking-visual-prompting-for-multimodal","title":"Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge","date":"2024-07-05","arxiv_id":"2407.04681","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-c-c-program-repair-for-high-level","title":"Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models","date":"2024-07-04","arxiv_id":"2407.03889","repositories_listed":0,"syntology":null},{"url":null,"slug":"casegpt-a-case-reasoning-framework-based-on","title":"CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation","date":"2024-07-04","arxiv_id":"2407.07913","repositories_listed":0,"syntology":null},{"url":null,"slug":"dslr-document-refinement-with-sentence-level","title":"DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation","date":"2024-07-04","arxiv_id":"2407.03627","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-dsl-code-generation","title":"A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation","date":"2024-07-03","arxiv_id":"2407.02742","repositories_listed":0,"syntology":null},{"url":"/paper/rankrag-unifying-context-ranking-with","slug":"rankrag-unifying-context-ranking-with","title":"RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs","date":"2024-07-02","arxiv_id":"2407.02485","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-advanced-large-language-models-with","title":"Exploring Advanced Large Language Models with LLMsuite","date":"2024-07-01","arxiv_id":"2407.12036","repositories_listed":0,"syntology":null},{"url":null,"slug":"face4rag-factual-consistency-evaluation-for","title":"Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese","date":"2024-07-01","arxiv_id":"2407.01080","repositories_listed":0,"syntology":null},{"url":null,"slug":"ground-every-sentence-improving-retrieval","title":"Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation","date":"2024-07-01","arxiv_id":"2407.01796","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-rag-empowered-multi-modal-llm-for","title":"Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach","date":"2024-07-01","arxiv_id":"2407.00978","repositories_listed":0,"syntology":null},{"url":null,"slug":"secgenai-enhancing-security-of-cloud-based","title":"SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest","date":"2024-07-01","arxiv_id":"2407.01110","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-memory-3-language-modeling-with-explicit","title":"$\\text{Memory}^3$: Language Modeling with Explicit Memory","date":"2024-07-01","arxiv_id":"2407.01178","repositories_listed":0,"syntology":null},{"url":null,"slug":"answering-real-world-clinical-questions-using","title":"Answering real-world clinical questions using large language model based systems","date":"2024-06-29","arxiv_id":"2407.00541","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-rag-to-riches-retrieval-interlaced-with","title":"From RAG to RICHES: Retrieval Interlaced with Sequence Generation","date":"2024-06-29","arxiv_id":"2407.00361","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm4design-an-automated-multi-modal-system","title":"LLM4DESIGN: An Automated Multi-Modal System for Architectural and Environmental Design","date":"2024-06-28","arxiv_id":"2407.12025","repositories_listed":0,"syntology":null},{"url":null,"slug":"sk-vqa-synthetic-knowledge-generation-at","title":"SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs","date":"2024-06-28","arxiv_id":"2406.19593","repositories_listed":0,"syntology":null},{"url":null,"slug":"autorag-hp-automatic-online-hyper-parameter","title":"AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation","date":"2024-06-27","arxiv_id":"2406.19251","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-and-evaluation-of-a-retrieval","title":"Development and Evaluation of a Retrieval-Augmented Generation Tool for Creating SAPPhIRE Models of Artificial Systems","date":"2024-06-27","arxiv_id":"2406.19493","repositories_listed":0,"syntology":null},{"url":null,"slug":"raven-multitask-retrieval-augmented-vision","title":"RAVEN: Multitask Retrieval Augmented Vision-Language Learning","date":"2024-06-27","arxiv_id":"2406.19150","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeing-is-believing-black-box-membership","title":"Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation","date":"2024-06-27","arxiv_id":"2406.19234","repositories_listed":0,"syntology":null},{"url":null,"slug":"which-neurons-matter-in-ir-applying","title":"Which Neurons Matter in IR? Applying Integrated Gradients-based Methods to Understand Cross-Encoders","date":"2024-06-27","arxiv_id":"2406.19309","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-native-memory-a-pathway-from-llms-towards","title":"AI-native Memory: A Pathway from LLMs Towards AGI","date":"2024-06-26","arxiv_id":"2406.18312","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-quality-of-answers-for-retrieval","title":"Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need","date":"2024-06-26","arxiv_id":"2406.18064","repositories_listed":0,"syntology":null},{"url":null,"slug":"glue-pizza-and-eat-rocks-exploiting","title":"\"Glue pizza and eat rocks\" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models","date":"2024-06-26","arxiv_id":"2406.19417","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-step-knowledge-retrieval-and-inference","title":"Multi-step Inference over Unstructured Data","date":"2024-06-26","arxiv_id":"2406.17987","repositories_listed":0,"syntology":null},{"url":null,"slug":"poisoned-langchain-jailbreak-llms-by","title":"Poisoned LangChain: Jailbreak LLMs by LangChain","date":"2024-06-26","arxiv_id":"2406.18122","repositories_listed":0,"syntology":null},{"url":null,"slug":"ragbench-explainable-benchmark-for-retrieval","title":"RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems","date":"2024-06-25","arxiv_id":"2407.11005","repositories_listed":0,"syntology":null},{"url":null,"slug":"context-augmented-retrieval-a-novel-framework","title":"Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model","date":"2024-06-24","arxiv_id":"2406.16383","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-augmented-llms-for-personalized-health","title":"Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis","date":"2024-06-24","arxiv_id":"2406.16252","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-role-of-long-tail-knowledge-in","title":"On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models","date":"2024-06-24","arxiv_id":"2406.16367","repositories_listed":0,"syntology":null},{"url":null,"slug":"found-in-the-middle-calibrating-positional","title":"Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization","date":"2024-06-23","arxiv_id":"2406.16008","repositories_listed":0,"syntology":null},{"url":null,"slug":"harnessing-knowledge-retrieval-with-large","title":"Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction","date":"2024-06-21","arxiv_id":"2406.15045","repositories_listed":0,"syntology":null},{"url":null,"slug":"longrag-enhancing-retrieval-augmented","title":"LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs","date":"2024-06-21","arxiv_id":"2406.15319","repositories_listed":0,"syntology":null},{"url":null,"slug":"pistis-rag-a-scalable-cascading-framework","title":"Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback","date":"2024-06-21","arxiv_id":"2407.00072","repositories_listed":0,"syntology":null},{"url":null,"slug":"temprompt-multi-task-prompt-learning-for","title":"TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems","date":"2024-06-21","arxiv_id":"2406.14825","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-retrieval-augmented-generation-over","title":"Towards Retrieval Augmented Generation over Large Video Libraries","date":"2024-06-21","arxiv_id":"2406.14938","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation-extraction-with-fine-tuned-large","title":"Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks","date":"2024-06-20","arxiv_id":"2406.14745","repositories_listed":0,"syntology":null},{"url":null,"slug":"ttqa-rs-a-break-down-prompting-approach-for","title":"TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization","date":"2024-06-20","arxiv_id":"2406.14732","repositories_listed":0,"syntology":null},{"url":null,"slug":"forag-factuality-optimized-retrieval","title":"FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering","date":"2024-06-19","arxiv_id":"2406.13779","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-zero-shot-llm-re-ranker-with-risk","title":"Improving Zero-shot LLM Re-Ranker with Risk Minimization","date":"2024-06-19","arxiv_id":"2406.13331","repositories_listed":0,"syntology":null},{"url":null,"slug":"thread-a-logic-based-data-organization","title":"Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13372","repositories_listed":0,"syntology":null},{"url":null,"slug":"wikicontradict-a-benchmark-for-evaluating","title":"WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia","date":"2024-06-19","arxiv_id":"2406.13805","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-selection-for-homogeneous-tools-an","title":"Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario","date":"2024-06-18","arxiv_id":"2406.12429","repositories_listed":0,"syntology":null},{"url":null,"slug":"debate-as-optimization-adaptive-conformal","title":"Debate as Optimization: Adaptive Conformal Prediction and Diverse Retrieval for Event Extraction","date":"2024-06-18","arxiv_id":"2406.12197","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-rags-to-rich-parameters-probing-how","title":"From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries","date":"2024-06-18","arxiv_id":"2406.12824","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-performance-sensitive","title":"Identifying Performance-Sensitive Configurations in Software Systems through Code Analysis with LLM Agents","date":"2024-06-18","arxiv_id":"2406.12806","repositories_listed":0,"syntology":null},{"url":null,"slug":"intermediate-distillation-data-efficient","title":"Intermediate Distillation: Data-Efficient Distillation from Black-Box LLMs for Information Retrieval","date":"2024-06-18","arxiv_id":"2406.12169","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-for-generative","title":"Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine","date":"2024-06-18","arxiv_id":"2406.12449","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-meets-reasoning-dynamic-in-context","title":"Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding","date":"2024-06-18","arxiv_id":"2406.12331","repositories_listed":0,"syntology":null},{"url":null,"slug":"richrag-crafting-rich-responses-for-multi","title":"RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation","date":"2024-06-18","arxiv_id":"2406.12566","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-biomedical-knowledge-retrieval","title":"SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation","date":"2024-06-17","arxiv_id":"2406.11258","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-or-fine-failing-debunking","title":"Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models","date":"2024-06-17","arxiv_id":"2406.11201","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-utility-judgment-framework-via-llms","title":"Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy","date":"2024-06-17","arxiv_id":"2406.11290","repositories_listed":0,"syntology":null},{"url":null,"slug":"tifg-text-informed-feature-generation-with","title":"Retrieval-Augmented Feature Generation for Domain-Specific Classification","date":"2024-06-17","arxiv_id":"2406.11177","repositories_listed":0,"syntology":null},{"url":null,"slug":"vul-rag-enhancing-llm-based-vulnerability","title":"Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG","date":"2024-06-17","arxiv_id":"2406.11147","repositories_listed":0,"syntology":null},{"url":null,"slug":"current-state-of-llm-risks-and-ai-guardrails","title":"Current state of LLM Risks and AI Guardrails","date":"2024-06-16","arxiv_id":"2406.12934","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-in-drug-safety-data-analysis","title":"Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL","date":"2024-06-15","arxiv_id":"2406.10690","repositories_listed":0,"syntology":null}],"record_sha256":"8bcc9f7511d47f78aaa6c2fb150a0837973b8479221cb47f5b712ee48c17a142","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}