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Retrieval-augmented Generation

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Showing 1901–1925 of 2196 papers

TitleStatusHype
Should We Fine-Tune or RAG? Evaluating Different Techniques to Adapt LLMs for DialogueCode0
Evaluating the Retrieval Component in LLM-Based Question Answering Systems—0
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented GenerationCode0
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents—0
Corpus Poisoning via Approximate Greedy Gradient DescentCode0
End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction—0
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential—0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor—0
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models—0
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks—0
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context UnderstandingCode0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models—0
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study—0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin—0
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation—0
SoccerRAG: Multimodal Soccer Information Retrieval via Natural QueriesCode0
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs—0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination—0
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs—0
Demo: Soccer Information Retrieval via Natural Queries using SoccerRAGCode0
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented GenerationCode0
Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning—0
RAG Does Not Work for Enterprises—0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
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