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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 1626–1650 of 2111 papers

TitleStatusHype
Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge—0
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning—0
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
CaseGPT: a case reasoning framework based on language models and retrieval-augmented generation—0
Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models—0
TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language ModelsCode2
DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation—0
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation—0
MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI ApplicationsCode1
RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs—0
MeMemo: On-device Retrieval Augmentation for Private and Personalized Text GenerationCode2
Why does in-context learning fail sometimes? Evaluating in-context learning on open and closed questionsCode0
Exploring Advanced Large Language Models with LLMsuite—0
BERGEN: A Benchmarking Library for Retrieval-Augmented GenerationCode3
Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach—0
Searching for Best Practices in Retrieval-Augmented GenerationCode3
Face4RAG: Factual Consistency Evaluation for Retrieval Augmented Generation in Chinese—0
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
Summary of a Haystack: A Challenge to Long-Context LLMs and RAG SystemsCode2
Memory^3: Language Modeling with Explicit Memory—0
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation—0
Retrieval-augmented generation in multilingual settingsCode3
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest—0
Large Language Models Struggle in Token-Level Clinical Named Entity RecognitionCode0
From RAG to RICHES: Retrieval Interlaced with Sequence Generation—0
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