Faithfulness-Aware Multi-Objective Context Ranking for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing ranking methods in RAG pipelines primarily optimize for document-query relevance, neglecting crucial factors for generation quality such as factual consistency and information coverage. We propose a novel multiobjective ranking framework that explicitly models three critical dimensions: relevance, coverage, and faithfulness support. Unlike traditional IR-centric approaches, our method introduces a utilitybased scoring mechanism that evaluates each document’s contribution to reducing hallucinations, improving answer completeness, and maintaining relevance. We formulate the ranking problem as a multi-objective optimization task and employ listwise learning with carefully constructed utility labels derived from existing QA datasets. Extensive experiments on Natural Questions, TriviaQA, and HotpotQA demonstrate that our approach achieves substantial improvements over state-of-the-art baselines, with an average 4.8- point increase (8.6% relative improvement) in Exact Match scores, 10.1% improvement in faithfulness metrics, and 35.7% reduction in hallucination rates compared to RankRAG, all while maintaining computational efficiency comparable to traditional reranking methods.