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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 1876–1900 of 2111 papers

TitleStatusHype
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation—0
Demo: Soccer Information Retrieval via Natural Queries using SoccerRAGCode0
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost—0
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
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts—0
Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation—0
Designing an Evaluation Framework for Large Language Models in Astronomy ResearchCode0
Phantom: General Trigger Attacks on Retrieval Augmented Language Generation—0
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools—0
Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension—0
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
Unlearning Climate Misinformation in Large Language Models—0
A Multi-Source Retrieval Question Answering Framework Based on RAG—0
Don't Forget to Connect! Improving RAG with Graph-based Reranking—0
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented GeneratorCode0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
Exploiting the Layered Intrinsic Dimensionality of Deep Models for Practical Adversarial Training—0
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling—0
Augmenting Textual Generation via Topology Aware Retrieval—0
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems—0
QUB-Cirdan at "Discharge Me!": Zero shot discharge letter generation by open-source LLM—0
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions—0
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