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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 1126–1150 of 2111 papers

TitleStatusHype
CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks—0
Calibrated Decision-Making through LLM-Assisted Retrieval—0
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models—0
CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
Can Language Models Enable In-Context Database?—0
Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets—0
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks—0
Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method—0
Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces—0
CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation—0
Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation—0
CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation—0
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation—0
CC-RAG: Structured Multi-Hop Reasoning via Theme-Based Causal Graphs—0
CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation—0
CCSK:Cognitive Convection of Self-Knowledge Based Retrieval Augmentation for Large Language Models—0
C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System—0
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs—0
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks—0
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device—0
Chain-of-Retrieval Augmented Generation—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Characterizing Network Structure of Anti-Trans Actors on TikTok—0
Characterizing the Dilemma of Performance and Index Size in Billion-Scale Vector Search and Breaking It with Second-Tier Memory—0
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