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Retrieval-augmented Generation

Papers

Showing 17761800 of 2196 papers

TitleStatusHype
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
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 Documents0
Corpus Poisoning via Approximate Greedy Gradient DescentCode0
CRAG -- Comprehensive RAG BenchmarkCode3
Multi-Head RAG: Solving Multi-Aspect Problems with LLMsCode3
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential0
End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts0
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks0
RATT: A Thought Structure for Coherent and Correct LLM ReasoningCode1
Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context UnderstandingCode0
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor0
UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models0
Ask-EDA: A Design Assistant Empowered by LLM, Hybrid RAG and Abbreviation De-hallucination0
Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin0
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs0
SoccerRAG: Multimodal Soccer Information Retrieval via Natural QueriesCode0
Demo: Soccer Information Retrieval via Natural Queries using SoccerRAGCode0
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs0
BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models0
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented GenerationCode1
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation0
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 study0
COS-Mix: Cosine Similarity and Distance Fusion for Improved Information RetrievalCode4
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