{"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/18","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":18,"pages_in_order":22,"rows_per_page":100,"rows":[1701,1800],"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/17","next":"/task/retrieval-augmented-generation/papers/19","papers":[{"url":null,"slug":"inference-scaling-for-long-context-retrieval","title":"Inference Scaling for Long-Context Retrieval Augmented Generation","date":"2024-10-06","arxiv_id":"2410.04343","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-performance-of-human-capable","title":"Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?","date":"2024-10-05","arxiv_id":"2410.16285","repositories_listed":0,"syntology":null},{"url":null,"slug":"metadata-based-data-exploration-with","title":"Metadata-based Data Exploration with Retrieval-Augmented Generation for Large Language Models","date":"2024-10-05","arxiv_id":"2410.04231","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-language-model-based-framework-for","title":"A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation","date":"2024-10-04","arxiv_id":"2410.09077","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-gda-automatic-domain-adaptation-for","title":"Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation","date":"2024-10-04","arxiv_id":"2410.03461","repositories_listed":0,"syntology":null},{"url":null,"slug":"misinformation-with-legal-consequences-mislc","title":"Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation","date":"2024-10-04","arxiv_id":"2410.03829","repositories_listed":0,"syntology":null},{"url":null,"slug":"orassistant-a-custom-rag-based-conversational","title":"ORAssistant: A Custom RAG-based Conversational Assistant for OpenROAD","date":"2024-10-04","arxiv_id":"2410.03845","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-frame-based-construction-of","title":"Scalable Frame-based Construction of Sociocultural NormBases for Socially-Aware Dialogues","date":"2024-10-04","arxiv_id":"2410.03049","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-of-retrieval-augmented","title":"A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions","date":"2024-10-03","arxiv_id":"2410.12837","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-in-large-language-models-yields","title":"Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers","date":"2024-10-03","arxiv_id":"2410.02642","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-specific-retrieval-augmented","title":"Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization","date":"2024-10-03","arxiv_id":"2410.02721","repositories_listed":0,"syntology":null},{"url":null,"slug":"grounding-large-language-models-in-embodied","title":"Grounding Large Language Models In Embodied Environment With Imperfect World Models","date":"2024-10-03","arxiv_id":"2410.02742","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-much-can-rag-help-the-reasoning-of-llm","title":"How Much Can RAG Help the Reasoning of LLM?","date":"2024-10-03","arxiv_id":"2410.02338","repositories_listed":0,"syntology":null},{"url":null,"slug":"intrinsic-evaluation-of-rag-systems-for-deep","title":"Intrinsic Evaluation of RAG Systems for Deep-Logic Questions","date":"2024-10-03","arxiv_id":"2410.02932","repositories_listed":0,"syntology":null},{"url":null,"slug":"iot-llm-enhancing-real-world-iot-task","title":"IoT-LLM: Enhancing Real-World IoT Task Reasoning with Large Language Models","date":"2024-10-03","arxiv_id":"2410.02429","repositories_listed":0,"syntology":null},{"url":null,"slug":"reward-rag-enhancing-rag-with-reward-driven","title":"Reward-RAG: Enhancing RAG with Reward Driven Supervision","date":"2024-10-03","arxiv_id":"2410.03780","repositories_listed":0,"syntology":null},{"url":null,"slug":"streamlining-conformal-information-retrieval","title":"Streamlining Conformal Information Retrieval via Score Refinement","date":"2024-10-03","arxiv_id":"2410.02914","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertaintyrag-span-level-uncertainty","title":"UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation","date":"2024-10-03","arxiv_id":"2410.02719","repositories_listed":0,"syntology":null},{"url":null,"slug":"undesirable-memorization-in-large-language","title":"Undesirable Memorization in Large Language Models: A Survey","date":"2024-10-03","arxiv_id":"2410.02650","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-further-elicit-reasoning-in-llms","title":"Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks","date":"2024-10-02","arxiv_id":"2410.01428","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-the-capabilities-of-compact-models","title":"Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation","date":"2024-10-01","arxiv_id":"2410.00387","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-peace-in-global-media-using-rag","title":"Classifying Peace in Global Media Using RAG and Intergroup Reciprocity","date":"2024-10-01","arxiv_id":"2410.13865","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-reliance-on-external-information","title":"Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis","date":"2024-10-01","arxiv_id":"2410.00857","repositories_listed":0,"syntology":null},{"url":null,"slug":"uniadapt-a-universal-adapter-for-knowledge","title":"UniAdapt: A Universal Adapter for Knowledge Calibration","date":"2024-10-01","arxiv_id":"2410.00454","repositories_listed":0,"syntology":null},{"url":null,"slug":"bsharedrag-backbone-shared-retrieval","title":"BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain","date":"2024-09-30","arxiv_id":"2409.20075","repositories_listed":0,"syntology":null},{"url":null,"slug":"pear-position-embedding-agnostic-attention-re","title":"PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead","date":"2024-09-29","arxiv_id":"2409.19745","repositories_listed":0,"syntology":null},{"url":null,"slug":"crafting-personalized-agents-through","title":"Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs","date":"2024-09-28","arxiv_id":"2409.19401","repositories_listed":0,"syntology":null},{"url":null,"slug":"healthq-unveiling-questioning-capabilities-of","title":"HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations","date":"2024-09-28","arxiv_id":"2409.19487","repositories_listed":0,"syntology":null},{"url":null,"slug":"thematic-analysis-with-open-source-generative","title":"Thematic Analysis with Open-Source Generative AI and Machine Learning: A New Method for Inductive Qualitative Codebook Development","date":"2024-09-28","arxiv_id":"2410.03721","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gen-ai-framework-for-medical-note","title":"A GEN AI Framework for Medical Note Generation","date":"2024-09-27","arxiv_id":"2410.01841","repositories_listed":0,"syntology":null},{"url":null,"slug":"aipatient-simulating-patients-with-ehrs-and","title":"AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow","date":"2024-09-27","arxiv_id":"2409.18924","repositories_listed":0,"syntology":null},{"url":null,"slug":"corpus-informed-retrieval-augmented","title":"Corpus-informed Retrieval Augmented Generation of Clarifying Questions","date":"2024-09-27","arxiv_id":"2409.18575","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-in-domain-question-answering-for","title":"Efficient In-Domain Question Answering for Resource-Constrained Environments","date":"2024-09-26","arxiv_id":"2409.17648","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodied-rag-general-non-parametric-embodied","title":"Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation","date":"2024-09-26","arxiv_id":"2409.18313","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-structured-data-retrieval-with","title":"Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study","date":"2024-09-26","arxiv_id":"2409.17580","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-tourism-recommender-systems-for","title":"Enhancing Tourism Recommender Systems for Sustainable City Trips Using Retrieval-Augmented Generation","date":"2024-09-26","arxiv_id":"2409.18003","repositories_listed":0,"syntology":null},{"url":null,"slug":"llama-sciq-an-educational-chatbot-for","title":"LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ","date":"2024-09-25","arxiv_id":"2409.16779","repositories_listed":0,"syntology":null},{"url":null,"slug":"asthmabot-multi-modal-multi-lingual-retrieval","title":"AsthmaBot: Multi-modal, Multi-Lingual Retrieval Augmented Generation For Asthma Patient Support","date":"2024-09-24","arxiv_id":"2409.15815","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyber-knowledge-completion-using-large","title":"Cyber Knowledge Completion Using Large Language Models","date":"2024-09-24","arxiv_id":"2409.16176","repositories_listed":0,"syntology":null},{"url":null,"slug":"lighter-and-better-towards-flexible-context","title":"Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation","date":"2024-09-24","arxiv_id":"2409.15699","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-framework-for-evaluating-api","title":"A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models","date":"2024-09-23","arxiv_id":"2409.15228","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-scientific-reproducibility-through","title":"Enhancing Scientific Reproducibility Through Automated BioCompute Object Creation Using Retrieval-Augmented Generation from Publications","date":"2024-09-23","arxiv_id":"2409.15076","repositories_listed":0,"syntology":null},{"url":null,"slug":"gem-rag-graphical-eigen-memories-for","title":"GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation","date":"2024-09-23","arxiv_id":"2409.15566","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-is-not-ready-for-clinical-use","title":"Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation","date":"2024-09-23","arxiv_id":"2409.15260","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-when-to-retrieve-what-to-rewrite-and","title":"Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA","date":"2024-09-23","arxiv_id":"2409.15515","repositories_listed":0,"syntology":null},{"url":null,"slug":"lessons-learned-on-information-retrieval-in","title":"Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies","date":"2024-09-23","arxiv_id":"2409.15163","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-rag-and-beyond","title":"Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely","date":"2024-09-23","arxiv_id":"2409.14924","repositories_listed":0,"syntology":null},{"url":null,"slug":"scideator-human-llm-scientific-idea","title":"Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination","date":"2024-09-23","arxiv_id":"2409.14634","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-words-evaluating-large-language-models","title":"Beyond Words: Evaluating Large Language Models in Transportation Planning","date":"2024-09-22","arxiv_id":"2409.14516","repositories_listed":0,"syntology":null},{"url":null,"slug":"2409-13992","title":"SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval","date":"2024-09-21","arxiv_id":"2409.13992","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-assistants-for-spaceflight-procedures","title":"AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues","date":"2024-09-21","arxiv_id":"2409.14206","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-in-triples-for-llms-enhancing-table","title":"Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction","date":"2024-09-21","arxiv_id":"2409.14192","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-automated-patent-workflows-ai","title":"Towards Automated Patent Workflows: AI-Orchestrated Multi-Agent Framework for Intellectual Property Management and Analysis","date":"2024-09-21","arxiv_id":"2409.19006","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-e-commerce-product-title","title":"Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models","date":"2024-09-19","arxiv_id":"2409.12880","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-test-generation-how-far","title":"Retrieval-Augmented Test Generation: How Far Are We?","date":"2024-09-19","arxiv_id":"2409.12682","repositories_listed":0,"syntology":null},{"url":null,"slug":"vera-validation-and-enhancement-for-retrieval","title":"VERA: Validation and Enhancement for Retrieval Augmented systems","date":"2024-09-18","arxiv_id":"2409.15364","repositories_listed":0,"syntology":null},{"url":null,"slug":"challenging-fairness-a-comprehensive","title":"Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness","date":"2024-09-17","arxiv_id":"2409.10825","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-context-faithfulness-in-large","title":"Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style","date":"2024-09-17","arxiv_id":"2409.10955","repositories_listed":0,"syntology":null},{"url":null,"slug":"p-rag-progressive-retrieval-augmented","title":"P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task","date":"2024-09-17","arxiv_id":"2409.11279","repositories_listed":0,"syntology":null},{"url":null,"slug":"supercoder2-0-technical-report-on-exploring","title":"SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmer","date":"2024-09-17","arxiv_id":"2409.11190","repositories_listed":0,"syntology":null},{"url":null,"slug":"lab-ai-retrieval-augmented-language-model-for","title":"Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine","date":"2024-09-16","arxiv_id":"2409.18986","repositories_listed":0,"syntology":null},{"url":null,"slug":"sfr-rag-towards-contextually-faithful-llms","title":"SFR-RAG: Towards Contextually Faithful LLMs","date":"2024-09-16","arxiv_id":"2409.09916","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-models-and-retrieval-augmented","title":"Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports","date":"2024-09-15","arxiv_id":"2409.10576","repositories_listed":0,"syntology":null},{"url":null,"slug":"hacking-the-lazy-way-llm-augmented-pentesting","title":"Hacking, The Lazy Way: LLM Augmented Pentesting","date":"2024-09-14","arxiv_id":"2409.09493","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-models-grok-to-copy","title":"Language Models \"Grok\" to Copy","date":"2024-09-14","arxiv_id":"2409.09281","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rag-approach-for-generating-competency","title":"A RAG Approach for Generating Competency Questions in Ontology Engineering","date":"2024-09-13","arxiv_id":"2409.08820","repositories_listed":0,"syntology":null},{"url":null,"slug":"la-rag-enhancing-llm-based-asr-accuracy-with","title":"LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation","date":"2024-09-13","arxiv_id":"2409.08597","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-q-a-text-retrieval-with-ranking","title":"Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG","date":"2024-09-12","arxiv_id":"2409.07691","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimenting-with-legal-ai-solutions-the","title":"Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice","date":"2024-09-12","arxiv_id":"2409.07713","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-vulnerability-of-applying-retrieval","title":"On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains","date":"2024-09-12","arxiv_id":"2409.17275","repositories_listed":0,"syntology":null},{"url":null,"slug":"2409-13741","title":"Knowing When to Ask -- Bridging Large Language Models and Data","date":"2024-09-10","arxiv_id":"2409.13741","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-large-language-model-driven","title":"Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles","date":"2024-09-10","arxiv_id":"2409.06450","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-correction-of-named","title":"Retrieval Augmented Correction of Named Entity Speech Recognition Errors","date":"2024-09-09","arxiv_id":"2409.06062","repositories_listed":0,"syntology":null},{"url":null,"slug":"ragent-retrieval-based-access-control-policy","title":"RAGent: Retrieval-based Access Control Policy Generation","date":"2024-09-08","arxiv_id":"2409.07489","repositories_listed":0,"syntology":null},{"url":null,"slug":"column-vocabulary-association-cva-semantic","title":"Column Vocabulary Association (CVA): semantic interpretation of dataless tables","date":"2024-09-06","arxiv_id":"2409.13709","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-based-incident","title":"Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support","date":"2024-09-06","arxiv_id":"2409.13707","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphinsight-unlocking-insights-in-large","title":"GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding","date":"2024-09-05","arxiv_id":"2409.03258","repositories_listed":0,"syntology":null},{"url":null,"slug":"marags-a-multi-adapter-system-for-multi-task","title":"MARAGS: A Multi-Adapter System for Multi-Task Retrieval Augmented Generation Question Answering","date":"2024-09-05","arxiv_id":"2409.03171","repositories_listed":0,"syntology":null},{"url":null,"slug":"rag-based-question-answering-for-contextual","title":"RAG based Question-Answering for Contextual Response Prediction System","date":"2024-09-05","arxiv_id":"2409.03708","repositories_listed":0,"syntology":null},{"url":null,"slug":"vietnamese-legal-information-retrieval-in","title":"Vietnamese Legal Information Retrieval in Question-Answering System","date":"2024-09-05","arxiv_id":"2409.13699","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-cyber-incident-timeline-analysis","title":"GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models","date":"2024-09-04","arxiv_id":"2409.02572","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-a-gen-ai-based-track-and-trace","title":"Creating a Gen-AI based Track and Trace Assistant MVP (SuperTracy) for PostNL","date":"2024-09-04","arxiv_id":"2409.02711","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversify-verify-adapt-efficient-and-robust","title":"Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering","date":"2024-09-04","arxiv_id":"2409.02361","repositories_listed":0,"syntology":null},{"url":null,"slug":"moa-is-all-you-need-building-llm-research","title":"MoA is All You Need: Building LLM Research Team using Mixture of Agents","date":"2024-09-04","arxiv_id":"2409.07487","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-cognitive-domains-for-llms","title":"Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture","date":"2024-09-03","arxiv_id":"2409.01556","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-defense-of-rag-in-the-era-of-long-context","title":"In Defense of RAG in the Era of Long-Context Language Models","date":"2024-09-03","arxiv_id":"2409.01666","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-role-of-large-language-models-in","title":"The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?","date":"2024-09-03","arxiv_id":"2409.01864","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learnable-agent-collaboration-network","title":"A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine","date":"2024-09-01","arxiv_id":"2409.00636","repositories_listed":0,"syntology":null},{"url":null,"slug":"genai-powered-multi-agent-paradigm-for-smart","title":"GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systems","date":"2024-08-31","arxiv_id":"2409.00494","repositories_listed":0,"syntology":null},{"url":null,"slug":"orthodoc-multimodal-large-language-model-for","title":"OrthoDoc: Multimodal Large Language Model for Assisting Diagnosis in Computed Tomography","date":"2024-08-30","arxiv_id":"2409.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"rissole-parameter-efficient-diffusion-models","title":"RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance","date":"2024-08-30","arxiv_id":"2408.17095","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypa-rag-a-hybrid-parameter-adaptive","title":"HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications","date":"2024-08-29","arxiv_id":"2409.09046","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-lossless-speculative-decoding-via","title":"Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation","date":"2024-08-28","arxiv_id":"2408.15562","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-text-summarization-factuality-using","title":"Measuring text summarization factuality using atomic facts entailment metrics in the context of retrieval augmented generation","date":"2024-08-27","arxiv_id":"2408.15171","repositories_listed":0,"syntology":null},{"url":null,"slug":"claim-verification-in-the-age-of-large","title":"Claim Verification in the Age of Large Language Models: A Survey","date":"2024-08-26","arxiv_id":"2408.14317","repositories_listed":0,"syntology":null},{"url":null,"slug":"probing-causality-manipulation-of-large","title":"Probing Causality Manipulation of Large Language Models","date":"2024-08-26","arxiv_id":"2408.14380","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-for-dynamic","title":"Retrieval Augmented Generation for Dynamic Graph Modeling","date":"2024-08-26","arxiv_id":"2408.14523","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-large-languages-models-seem-not-to","title":"Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data","date":"2024-08-25","arxiv_id":"2408.13833","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-reliable-medical-question-answering","title":"Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models","date":"2024-08-25","arxiv_id":"2408.13808","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-human-level-understanding-of-complex","title":"Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering","date":"2024-08-24","arxiv_id":"2409.00082","repositories_listed":0,"syntology":null}],"record_sha256":"f830e426812b0338b90bd62b5e1606bb99f64b0282672292c33ce68c460a0906","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}