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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 1951–1975 of 2111 papers

TitleStatusHype
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented GenerationCode2
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification—0
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering CapabilityCode0
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question AnsweringCode1
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation SystemsCode1
Evaluating Very Long-Term Conversational Memory of LLM AgentsCode1
Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent SetupCode0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
From RAGs to riches: Using large language models to write documents for clinical trials—0
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)Code2
Fine-tuning Large Language Models for Domain-specific Machine Translation—0
Assessing generalization capability of text ranking models in Polish—0
2D Matryoshka Sentence EmbeddingsCode4
ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented AgentsCode2
FinBen: A Holistic Financial Benchmark for Large Language ModelsCode4
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data—0
Benchmarking Retrieval-Augmented Generation for MedicineCode4
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation—0
What Evidence Do Language Models Find Convincing?Code1
Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge—0
EVOR: Evolving Retrieval for Code GenerationCode2
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning—0
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning—0
Where is the answer? Investigating Positional Bias in Language Model Knowledge ExtractionCode0
Dense Passage Retrieval: Is it Retrieving?—0
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