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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 1501–1525 of 2111 papers

TitleStatusHype
Assessing generalization capability of text ranking models in Polish—0
Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?—0
Assessing the Robustness of Retrieval-Augmented Generation Systems in K-12 Educational Question Answering with Knowledge Discrepancies—0
ASTRAL: Automated Safety Testing of Large Language Models—0
ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems—0
A Study on the Implementation Method of an Agent-Based Advanced RAG System Using Graph—0
A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecture—0
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models—0
A Survey of Context Engineering for Large Language Models—0
A Survey of Multimodal Retrieval-Augmented Generation—0
A Survey of Query Optimization in Large Language Models—0
A Survey on Knowledge-Oriented Retrieval-Augmented Generation—0
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models—0
A Survey on Retrieval-Augmented Text Generation for Large Language Models—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
How Does Knowledge Selection Help Retrieval Augmented Generation?—0
AgentOps: Enabling Observability of LLM Agents—0
Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models—0
AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs—0
AttentionRAG: Attention-Guided Context Pruning in Retrieval-Augmented Generation—0
Attention with Dependency Parsing Augmentation for Fine-Grained Attribution—0
Attributing Response to Context: A Jensen-Shannon Divergence Driven Mechanistic Study of Context Attribution in Retrieval-Augmented Generation—0
Audiobox TTA-RAG: Improving Zero-Shot and Few-Shot Text-To-Audio with Retrieval-Augmented Generation—0
Enhancing Retrieval-Augmented Audio Captioning with Generation-Assisted Multimodal Querying and Progressive Learning—0
Augmenting LLM Reasoning with Dynamic Notes Writing for Complex QA—0
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