Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection

• URL: http://arxiv.org/abs/2601.17532v1
• Date: Sat, 24 Jan 2026 17:14:10 GMT
• Title: Less is More for RAG: Information Gain Pruning for Generator-Aligned Reranking and Evidence Selection
• Abstract summary: RAG grounds large language models with external evidence, but under a limited context budget, the key challenge is deciding which retrieved passages should be injected.<n>We show that retrieval relevance metrics correlate weakly with end-to-end QA quality and can even become negatively correlated under multi-passage injection.<n>We propose textbfInformation Gain Pruning (IGP), a deployment-friendly reranking-and-pruning module that selects evidence using a generator-aligned utility signal and filters weak or harmful passages before truncation.
• Score: 26.57845149504473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) grounds large language models with external evidence, but under a limited context budget, the key challenge is deciding which retrieved passages should be injected. We show that retrieval relevance metrics (e.g., NDCG) correlate weakly with end-to-end QA quality and can even become negatively correlated under multi-passage injection, where redundancy and mild conflicts destabilize generation. We propose \textbf{Information Gain Pruning (IGP)}, a deployment-friendly reranking-and-pruning module that selects evidence using a generator-aligned utility signal and filters weak or harmful passages before truncation, without changing existing budget interfaces. Across five open-domain QA benchmarks and multiple retrievers and generators, IGP consistently improves the quality--cost trade-off. In a representative multi-evidence setting, IGP delivers about +12--20% relative improvement in average F1 while reducing final-stage input tokens by roughly 76--79% compared to retriever-only baselines.

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