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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 1101–1125 of 2111 papers

TitleStatusHype
Bias Amplification in RAG: Poisoning Knowledge Retrieval to Steer LLMs—0
Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation—0
BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems—0
Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph—0
Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier—0
BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions—0
Bisecting K-Means in RAG for Enhancing Question-Answering Tasks Performance in Telecommunications—0
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models—0
Blowfish: Topological and statistical signatures for quantifying ambiguity in semantic search—0
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check—0
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation—0
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents—0
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation—0
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs—0
Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases through Text-to-NoSQL Translation—0
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL—0
Bridging the Preference Gap between Retrievers and LLMs—0
BRIT: Bidirectional Retrieval over Unified Image-Text Graph—0
BR-TaxQA-R: A Dataset for Question Answering with References for Brazilian Personal Income Tax Law, including case law—0
BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain—0
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)—0
C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like Retrieval-Augmented Generation—0
Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation—0
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability—0
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