PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
Jiayi Wu, Hengyi Cai, Lingyong Yan, Hao Sun, Xiang Li, Shuaiqiang Wang, Dawei Yin, Ming Gao·December 19, 2024
Summary
PA-RAG optimizes retrieval-augmented generation (RAG) systems, enhancing response quality, robustness, and citation accuracy. It introduces a method for constructing high-quality instruction fine-tuning data and multi-perspective preference data, optimizing a large language model through supervised fine-tuning and direct preference optimization. This approach improves performance across various datasets and language models, focusing on response correctness, citation recall, and precision. PA-RAG optimizes preference information in RAG scenarios, demonstrating significant enhancements in response quality across different benchmarks and language models.
Introduction
Background
Overview of retrieval-augmented generation (RAG) systems
Challenges in RAG systems: response quality, robustness, and citation accuracy
Objective
Aim of PA-RAG: enhancing RAG systems through optimized fine-tuning and preference optimization
Method
Data Construction
Creation of high-quality instruction fine-tuning data
Generation of multi-perspective preference data
Supervised Fine-Tuning
Process of fine-tuning a large language model with optimized data
Direct Preference Optimization
Method for optimizing preference information in RAG scenarios
Performance Evaluation
Across Datasets
Improvement in response quality, robustness, and citation accuracy
Language Models
Enhanced performance across different language models
Metrics
Focus on response correctness, citation recall, and precision
Results
Benchmark Comparisons
PA-RAG's performance against baseline RAG systems
Case Studies
Detailed analysis of PA-RAG's impact on response quality
Conclusion
Summary of Contributions
PA-RAG's advancements in RAG systems
Future Directions
Potential areas for further research and development
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What methods does PA-RAG use to construct high-quality instruction fine-tuning data and multi-perspective preference data?
What is PA-RAG and how does it optimize retrieval-augmented generation (RAG) systems?
How does PA-RAG optimize a large language model and what does it focus on improving?
What are the benchmarks and language models used to demonstrate the enhancements in response quality achieved by PA-RAG?