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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 301–325 of 2111 papers

TitleStatusHype
An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education—0
Osiris: A Lightweight Open-Source Hallucination Detection System—0
Retrieval Augmented Generation Evaluation for Health Documents—0
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights—0
Fine-Tuning Large Language Models and Evaluating Retrieval Methods for Improved Question Answering on Building Codes—0
LLM-Independent Adaptive RAG: Let the Question Speak for Itself—0
The Aloe Family Recipe for Open and Specialized Healthcare LLMs—0
A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models—0
Benchmarking LLM Faithfulness in RAG with Evolving LeaderboardsCode1
A Reasoning-Focused Legal Retrieval Benchmark—0
RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation—0
An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation—0
Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation—0
Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces—0
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language ModelsCode3
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
SymbioticRAG: Enhancing Document Intelligence Through Human-LLM Symbiotic Collaboration—0
SafeMate: A Modular RAG-Based Agent for Context-Aware Emergency Guidance—0
Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-PracticingCode1
Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair UseCode0
Retrieval-augmented in-context learning for multimodal large language models in disease classification—0
A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking—0
Real-time Spatial Retrieval Augmented Generation for Urban Environments—0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications—0
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