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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 1551–1575 of 2111 papers

TitleStatusHype
Bayesian inference to improve quality of Retrieval Augmented Generation—0
BEAVER: An Enterprise Benchmark for Text-to-SQL—0
Benchmarking Cognitive Domains for LLMs: Insights from Taiwanese Hakka Culture—0
Benchmarking Poisoning Attacks against Retrieval-Augmented Generation—0
Benchmarking Retrieval-Augmented Generation for Chemistry—0
Beyond Chains: Bridging Large Language Models and Knowledge Bases in Complex Question Answering—0
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models—0
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations—0
Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning—0
Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform—0
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation—0
Beyond Words: AuralLLM and SignMST-C for Precise Sign Language Production and Bidirectional Accessibility—0
Beyond Words: Evaluating Large Language Models in Transportation Planning—0
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis—0
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
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