PFE-SELF-RAG: Balancing self-RAG evaluation metrics via Pareto efficiency

Self-RAG enhances Retrieval-Augmented Generation (RAG) by enabling Large Language Models (LLMs)  to dynamically retrieve external knowledge and self-evaluate outputs. However, the original Self-RAG heavily relies on a manually tuned weighted-sum mechanism for combining critique scores, rendering the system brittle and poorly adaptable to diverse query distributions. To address these limitations, Pareto Front Enhanced Self-RAG (PFE-SELF-RAG) is proposed as a tuning-free Multi-Objective Optimization(MOO) framework. It first applies Maximal Marginal Relevance (MMR) to enrich context diversity, then incorporates two evaluation strategies: Pareto Front-based selection and Geometric Mean (GM) Aggregation. The primary significance of this approach lies in eliminating fragile manual weight tuning. By mathematically modeling the trade-off between factual accuracy and relevance, PFE-SELF-RAG enables adaptive candidate selection, allowing the number and quality of outputs to vary dynamically. This represents the first formal application of Pareto optimization to candidate ranking in self-reflective RAG systems, establishing a principled alternative to heuristic aggregation. Evaluations on PopQA, ARC Challenge, PubHealth, and TriviaQA demonstrate substantial impact. The Full Pareto Set strategy consistently outperforms the Self-RAG baseline, achieving %۵۸.۶ on PopQA (%+۳.۷), %۶۸.۰ on ARC Challenge (%+۱.۶), %۷۳.۰ on PubHealth (%+۰.۶), and %۷۱.۳ on TriviaQA (%+۴.۳). These improvements underscore the practical impact of replacing brittle heuristics with principled optimization, establishing PFE-SELF-RAG as a robust and scalable standard for self-reflective RAG systems.

کلیدواژه ها:

Retrieval-Augmented Generation ، Pareto Optimization ، Geometric Mean ، Self-RAG ، LLM Evaluation