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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 2051–2075 of 2111 papers

TitleStatusHype
SenTest: Evaluating Robustness of Sentence Encoders—0
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model—0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination—0
Minimizing Factual Inconsistency and Hallucination in Large Language Models—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation SystemsCode2
Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users—0
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking—0
Enhancing LLM Intelligence with ARM-RAG: Auxiliary Rationale Memory for Retrieval Augmented Generation—0
AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMsCode0
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval—0
FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction—0
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionCode4
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language ModelCode1
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models—0
Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language ModelsCode1
LLM4VV: Developing LLM-Driven Testsuite for Compiler ValidationCode0
Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human PreferenceCode1
Intuitive or Dependent? Investigating LLMs' Behavior Style to Conflicting Prompts—0
Chatmap : Large Language Model Interaction with Cartographic Data—0
MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering—0
RAGAS: Automated Evaluation of Retrieval Augmented GenerationCode6
Benchmarking Large Language Models in Retrieval-Augmented GenerationCode2
A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture—0
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language ModelsCode1
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