{"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/16","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":16,"pages_in_order":22,"rows_per_page":100,"rows":[1501,1600],"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/15","next":"/task/retrieval-augmented-generation/papers/17","papers":[{"url":null,"slug":"accelerating-manufacturing-scale-up-from","title":"Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design","date":"2024-12-08","arxiv_id":"2412.05937","repositories_listed":0,"syntology":null},{"url":null,"slug":"deco-life-cycle-management-of-enterprise","title":"DECO: Life-Cycle Management of Enterprise-Grade Copilots","date":"2024-12-08","arxiv_id":"2412.06099","repositories_listed":0,"syntology":null},{"url":null,"slug":"gee-ops-an-operator-knowledge-base-for","title":"GEE-OPs: An Operator Knowledge Base for Geospatial Code Generation on the Google Earth Engine Platform Powered by Large Language Models","date":"2024-12-07","arxiv_id":"2412.05587","repositories_listed":0,"syntology":null},{"url":null,"slug":"sla-management-in-reconfigurable-multi-agent","title":"SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering","date":"2024-12-07","arxiv_id":"2412.06832","repositories_listed":0,"syntology":null},{"url":null,"slug":"100-hallucination-elimination-using-acurai","title":"100% Elimination of Hallucinations on RAGTruth for GPT-4 and GPT-3.5 Turbo","date":"2024-12-06","arxiv_id":"2412.05223","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-based-approach-for-conversational-ai","title":"TOBUGraph: Knowledge Graph-Based Retrieval for Enhanced LLM Performance Beyond RAG","date":"2024-12-06","arxiv_id":"2412.05447","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-cross-language-code-translation-via","title":"Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation","date":"2024-12-06","arxiv_id":"2412.05159","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-retrieval-augmented","title":"Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy","date":"2024-12-06","arxiv_id":"2412.04697","repositories_listed":0,"syntology":null},{"url":null,"slug":"queen-a-large-language-model-for-quechua","title":"QueEn: A Large Language Model for Quechua-English Translation","date":"2024-12-06","arxiv_id":"2412.05184","repositories_listed":0,"syntology":null},{"url":null,"slug":"question-answering-for-decisionmaking-in","title":"Question Answering for Decisionmaking in Green Building Design: A Multimodal Data Reasoning Method Driven by Large Language Models","date":"2024-12-06","arxiv_id":"2412.04741","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-hallucinations-with-rag-and-nmiss","title":"Addressing Hallucinations with RAG and NMISS in Italian Healthcare LLM Chatbots","date":"2024-12-05","arxiv_id":"2412.04235","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-ai-text-generation-retrieval","title":"Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overview","date":"2024-12-05","arxiv_id":"2412.03933","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-large-language-models-to-generate-1","title":"Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects","date":"2024-12-05","arxiv_id":"2412.04185","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-on-scientific-knowledge-extraction","title":"A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences","date":"2024-12-04","arxiv_id":"2412.03531","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-conversational-psychotherapy","title":"Advancing Conversational Psychotherapy: Integrating Privacy, Dual-Memory, and Domain Expertise with Large Language Models","date":"2024-12-04","arxiv_id":"2412.02987","repositories_listed":0,"syntology":null},{"url":null,"slug":"caisson-concept-augmented-inference-suite-of","title":"CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks","date":"2024-12-03","arxiv_id":"2412.02835","repositories_listed":0,"syntology":null},{"url":null,"slug":"composing-open-domain-vision-with-rag-for","title":"Composing Open-domain Vision with RAG for Ocean Monitoring and Conservation","date":"2024-12-03","arxiv_id":"2412.02262","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-large-language-models-to","title":"Leveraging Large Language Models to Democratize Access to Costly Datasets for Academic Research","date":"2024-12-03","arxiv_id":"2412.02065","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-tokens-in-retrieval-augmented","title":"Semantic Tokens in Retrieval Augmented Generation","date":"2024-12-03","arxiv_id":"2412.02563","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-performance-explanation-through-large","title":"Query Performance Explanation through Large Language Model for HTAP Systems","date":"2024-12-02","arxiv_id":"2412.01709","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-llm-for-automated-ontology","title":"Leveraging LLM for Automated Ontology Extraction and Knowledge Graph Generation","date":"2024-11-30","arxiv_id":"2412.00608","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-strategic-mechanism-design-in-the","title":"Rethinking Strategic Mechanism Design In The Age Of Large Language Models: New Directions For Communication Systems","date":"2024-11-30","arxiv_id":"2412.00495","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-system-integration-analyzing-openapi","title":"Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation","date":"2024-11-29","arxiv_id":"2411.19804","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-a-low-code-complete-workflow-via","title":"Generating a Low-code Complete Workflow via Task Decomposition and RAG","date":"2024-11-29","arxiv_id":"2412.00239","repositories_listed":0,"syntology":null},{"url":null,"slug":"know-your-rag-dataset-taxonomy-and-generation","title":"Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems","date":"2024-11-29","arxiv_id":"2411.19710","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-management-for-automobile-failure","title":"Knowledge Management for Automobile Failure Analysis Using Graph RAG","date":"2024-11-29","arxiv_id":"2411.19539","repositories_listed":0,"syntology":null},{"url":null,"slug":"ragdiffusion-faithful-cloth-generation-via","title":"RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation","date":"2024-11-29","arxiv_id":"2411.19528","repositories_listed":0,"syntology":null},{"url":null,"slug":"sims-simulating-human-scene-interactions-with","title":"SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation","date":"2024-11-29","arxiv_id":"2411.19921","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-retrieval-accuracy-and","title":"Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems","date":"2024-11-29","arxiv_id":"2411.19463","repositories_listed":0,"syntology":null},{"url":null,"slug":"unimib-assistant-designing-a-student-friendly","title":"Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needs","date":"2024-11-29","arxiv_id":"2411.19554","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-indexing-internet-search-augmented","title":"Zero-Indexing Internet Search Augmented Generation for Large Language Models","date":"2024-11-29","arxiv_id":"2411.19478","repositories_listed":0,"syntology":null},{"url":null,"slug":"habit-coach-customising-rag-based-chatbots-to","title":"Habit Coach: Customising RAG-based chatbots to support behavior change","date":"2024-11-28","arxiv_id":"2411.19229","repositories_listed":0,"syntology":null},{"url":null,"slug":"iclerb-in-context-learning-embedding-and","title":"ICLERB: In-Context Learning Embedding and Reranker Benchmark","date":"2024-11-28","arxiv_id":"2411.18947","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-database-or-poison-base-detecting","title":"RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis","date":"2024-11-28","arxiv_id":"2411.18948","repositories_listed":0,"syntology":null},{"url":null,"slug":"way-to-specialist-closing-loop-between","title":"Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph","date":"2024-11-28","arxiv_id":"2411.19064","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-literature-review-using-nlp","title":"Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation","date":"2024-11-27","arxiv_id":"2411.18583","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-calibrated-automated-testing-and","title":"Human-Calibrated Automated Testing and Validation of Generative Language Models","date":"2024-11-25","arxiv_id":"2411.16391","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-mteb-to-mtob-retrieval-augmented","title":"From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive Grammars","date":"2024-11-23","arxiv_id":"2411.15577","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-factuality-of-3d-brain-mri-report","title":"Improving Factuality of 3D Brain MRI Report Generation with Paired Image-domain Retrieval and Text-domain Augmentation","date":"2024-11-23","arxiv_id":"2411.15490","repositories_listed":0,"syntology":null},{"url":null,"slug":"mr-2-ag-multimodal-retrieval-reflection","title":"mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA","date":"2024-11-22","arxiv_id":"2411.15041","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-llms-for-power-system-simulations-a","title":"Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework","date":"2024-11-21","arxiv_id":"2411.16707","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastrag-retrieval-augmented-generation-for","title":"FastRAG: Retrieval Augmented Generation for Semi-structured Data","date":"2024-11-21","arxiv_id":"2411.13773","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-context-rich-automated-biodiversity","title":"Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data","date":"2024-11-21","arxiv_id":"2411.14219","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-knowledge-checking-in-retrieval","title":"Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective","date":"2024-11-21","arxiv_id":"2411.14572","repositories_listed":0,"syntology":null},{"url":null,"slug":"aidbench-a-benchmark-for-evaluating-the","title":"AIDBench: A benchmark for evaluating the authorship identification capability of large language models","date":"2024-11-20","arxiv_id":"2411.13226","repositories_listed":0,"syntology":null},{"url":null,"slug":"dmqr-rag-diverse-multi-query-rewriting-for","title":"DMQR-RAG: Diverse Multi-Query Rewriting for RAG","date":"2024-11-20","arxiv_id":"2411.13154","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-large-language-model-for-wheat","title":"Multimodal large language model for wheat breeding: a new exploration of smart breeding","date":"2024-11-20","arxiv_id":"2411.15203","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-for-domain-1","title":"Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU","date":"2024-11-20","arxiv_id":"2411.13691","repositories_listed":0,"syntology":null},{"url":null,"slug":"writing-style-matters-an-examination-of-bias","title":"Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems","date":"2024-11-20","arxiv_id":"2411.13173","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-legal-companion-enhancing-access-to","title":"AI Legal Companion: Enhancing Access to Justice and Legal Literacy for the Public","date":"2024-11-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"codexembed-a-generalist-embedding-model","title":"CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval","date":"2024-11-19","arxiv_id":"2411.12644","repositories_listed":0,"syntology":null},{"url":null,"slug":"cue-m-contextual-understanding-and-enhanced","title":"CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model","date":"2024-11-19","arxiv_id":"2411.12287","repositories_listed":0,"syntology":null},{"url":null,"slug":"streetviewllm-extracting-geographic","title":"StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model","date":"2024-11-19","arxiv_id":"2411.14476","repositories_listed":0,"syntology":null},{"url":null,"slug":"textsc-neon-news-entity-interaction","title":"Neon: News Entity-Interaction Extraction for Enhanced Question Answering","date":"2024-11-19","arxiv_id":"2411.12449","repositories_listed":0,"syntology":null},{"url":null,"slug":"molecule-generation-with-fragment-retrieval","title":"Molecule Generation with Fragment Retrieval Augmentation","date":"2024-11-18","arxiv_id":"2411.12078","repositories_listed":0,"syntology":null},{"url":null,"slug":"addrllm-address-rewriting-via-large-language","title":"AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data","date":"2024-11-17","arxiv_id":"2411.13584","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-assisted-physical-invariant-extraction","title":"INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection","date":"2024-11-17","arxiv_id":"2411.10918","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-board-vision-language-models-for","title":"On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation","date":"2024-11-17","arxiv_id":"2411.11913","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-approach-to-eliminating","title":"A Novel Approach to Eliminating Hallucinations in Large Language Model-Assisted Causal Discovery","date":"2024-11-16","arxiv_id":"2411.12759","repositories_listed":0,"syntology":null},{"url":null,"slug":"adopting-rag-for-llm-aided-future-vehicle","title":"Adopting RAG for LLM-Aided Future Vehicle Design","date":"2024-11-14","arxiv_id":"2411.09590","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-and-practical-evaluation-of","title":"Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering","date":"2024-11-14","arxiv_id":"2411.09213","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-llms-prescient-a-continuous-evaluation","title":"Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle","date":"2024-11-13","arxiv_id":"2411.08324","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-trustworthy-ai-transparent-ai","title":"Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)","date":"2024-11-13","arxiv_id":"2411.08469","repositories_listed":0,"syntology":null},{"url":null,"slug":"refining-translations-with-llms-a-constraint","title":"Refining Translations with LLMs: A Constraint-Aware Iterative Prompting Approach","date":"2024-11-13","arxiv_id":"2411.08348","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-evaluating-large-language-models-for","title":"Towards Evaluating Large Language Models for Graph Query Generation","date":"2024-11-13","arxiv_id":"2411.08449","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-optimizing-a-retrieval-augmented","title":"Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data","date":"2024-11-13","arxiv_id":"2411.08438","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-optimization-for-parametric-knowledge","title":"Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models","date":"2024-11-12","arxiv_id":"2411.07820","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustful-llms-customizing-and-grounding-text","title":"Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders","date":"2024-11-12","arxiv_id":"2411.07870","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlocking-legal-knowledge-with-multi-layered","title":"Unlocking Legal Knowledge with Multi-Layered Embedding-Based Retrieval","date":"2024-11-12","arxiv_id":"2411.07739","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-primer-on-word-embeddings-ai-techniques-for","title":"A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work","date":"2024-11-11","arxiv_id":"2411.07156","repositories_listed":0,"syntology":null},{"url":null,"slug":"invar-rag-invariant-llm-aligned-retrieval-for","title":"Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation","date":"2024-11-11","arxiv_id":"2411.07021","repositories_listed":0,"syntology":null},{"url":null,"slug":"openthaigpt-1-5-a-thai-centric-open-source","title":"OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model","date":"2024-11-11","arxiv_id":"2411.07238","repositories_listed":0,"syntology":null},{"url":null,"slug":"lprotector-an-llm-driven-vulnerability","title":"LProtector: An LLM-driven Vulnerability Detection System","date":"2024-11-10","arxiv_id":"2411.06493","repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-algorithms-and-rag-enhancing-semi","title":"Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs","date":"2024-11-09","arxiv_id":"2411.06175","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-knowledge-boundaries-in-large","title":"Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment","date":"2024-11-09","arxiv_id":"2411.06207","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-retrieval-augmented-generation-for-1","title":"Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval","date":"2024-11-09","arxiv_id":"2411.06237","repositories_listed":0,"syntology":null},{"url":null,"slug":"sufficient-context-a-new-lens-on-retrieval","title":"Sufficient Context: A New Lens on Retrieval Augmented Generation Systems","date":"2024-11-09","arxiv_id":"2411.06037","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-cluster-resilience-llm-agent-based","title":"Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework","date":"2024-11-08","arxiv_id":"2411.05349","repositories_listed":0,"syntology":null},{"url":null,"slug":"qwen2-5-32b-leveraging-self-consistent-tool","title":"Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving","date":"2024-11-08","arxiv_id":"2411.05934","repositories_listed":0,"syntology":null},{"url":null,"slug":"audiobox-tta-rag-improving-zero-shot-and-few","title":"Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation","date":"2024-11-07","arxiv_id":"2411.05141","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-r-a-framework-for-domain-adaptive","title":"LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG","date":"2024-11-07","arxiv_id":"2411.04476","repositories_listed":0,"syntology":null},{"url":null,"slug":"m3docrag-multi-modal-retrieval-is-what-you","title":"M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding","date":"2024-11-07","arxiv_id":"2411.04952","repositories_listed":0,"syntology":null},{"url":null,"slug":"ml-promise-a-multilingual-dataset-for","title":"ML-Promise: A Multilingual Dataset for Corporate Promise Verification","date":"2024-11-07","arxiv_id":"2411.04473","repositories_listed":0,"syntology":null},{"url":null,"slug":"advanced-rag-models-with-graph-structures","title":"Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation","date":"2024-11-06","arxiv_id":"2411.03572","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-security-control-production-with","title":"Enhancing Security Control Production With Generative AI","date":"2024-11-06","arxiv_id":"2411.04284","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-guidance-for-retrievers","title":"Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation","date":"2024-11-06","arxiv_id":"2411.03957","repositories_listed":0,"syntology":null},{"url":null,"slug":"lego-graphrag-modularizing-graph-based","title":"LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration","date":"2024-11-06","arxiv_id":"2411.05844","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-context-rag-performance-of-large","title":"Long Context RAG Performance of Large Language Models","date":"2024-11-05","arxiv_id":"2411.03538","repositories_listed":0,"syntology":null},{"url":null,"slug":"persianrag-a-retrieval-augmented-generation","title":"PersianRAG: A Retrieval-Augmented Generation System for Persian Language","date":"2024-11-05","arxiv_id":"2411.02832","repositories_listed":0,"syntology":null},{"url":null,"slug":"washtsapp-a-rag-powered-whatsapp-chatbot-for","title":"WASHtsApp -- A RAG-powered WhatsApp Chatbot for supporting rural African clean water access, sanitation and hygiene","date":"2024-11-05","arxiv_id":"2411.02850","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-language-models-enable-in-context","title":"Can Language Models Enable In-Context Database?","date":"2024-11-04","arxiv_id":"2411.01807","repositories_listed":0,"syntology":null},{"url":null,"slug":"ruag-learned-rule-augmented-generation-for","title":"RuAG: Learned-rule-augmented Generation for Large Language Models","date":"2024-11-04","arxiv_id":"2411.03349","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-extraction-attacks-in-retrieval","title":"Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors","date":"2024-11-03","arxiv_id":"2411.01705","repositories_listed":0,"syntology":null},{"url":null,"slug":"lora-contextualizing-adaptation-of-large","title":"LoRA-Contextualizing Adaptation of Large Multimodal Models for Long Document Understanding","date":"2024-11-02","arxiv_id":"2411.01106","repositories_listed":0,"syntology":null},{"url":null,"slug":"attackqa-development-and-adoption-of-a","title":"AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs","date":"2024-11-01","arxiv_id":"2411.01073","repositories_listed":0,"syntology":null},{"url":null,"slug":"corag-a-cost-constrained-retrieval","title":"CORAG: A Cost-Constrained Retrieval Optimization System for Retrieval-Augmented Generation","date":"2024-11-01","arxiv_id":"2411.00744","repositories_listed":0,"syntology":null},{"url":null,"slug":"e2e-afg-an-end-to-end-model-with-adaptive","title":"E2E-AFG: An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation","date":"2024-11-01","arxiv_id":"2411.00437","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-ref-enhancing-reference-handling-in","title":"LLM-Ref: Enhancing Reference Handling in Technical Writing with Large Language Models","date":"2024-11-01","arxiv_id":"2411.00294","repositories_listed":0,"syntology":null},{"url":null,"slug":"provenance-a-light-weight-fact-checker-for","title":"Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output","date":"2024-11-01","arxiv_id":"2411.01022","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multi-source-retrieval-augmented","title":"Towards Multi-Source Retrieval-Augmented Generation via Synergizing Reasoning and Preference-Driven Retrieval","date":"2024-11-01","arxiv_id":"2411.00689","repositories_listed":0,"syntology":null}],"record_sha256":"5f2177bdf7897cef0df3d0dca5a26da773f4929a92fb1a5cdb23fe0a006cb516","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}