NeurIPS 2025

Influence Guided Context Selection for Effective
Retrieval-Augmented Generation
Jiale Deng, Yanyan Shen∗, Ziyuan Pei, Youmin Chen, Linpeng Huang
Shanghai Jiao Tong University
{jialedeng, shenyy, pzy_live, chenyoumin, lphuang}@sjtu.edu.cn
Abstract
Retrieval-Augmented Generation (RAG) addresses large language model (LLM)
hallucinations by grounding responses in external knowledge, but its effectiveness
is compromised by poor-quality retrieved contexts containing irrelevant or noisy
information. While existing approaches attempt to improve performance through
context selection based on predefined context quality assessment metrics, they
show limited gains over standard RAG. We attribute this limitation to their failure
in holistically utilizing available information (query, context list, and generator)
for comprehensive quality assessment. Inspired by recent advances in data se-
lection, we reconceptualize context quality assessment as an inference-time data
valuation problem and introduce the Contextual Influence Value (CI value). This
novel metric quantifies context quality by measuring the performance degradation
when removing each context from the list, effectively integrating query-aware rele-
vance, list-aware uniqueness, and generator-aware alignment. Moreover, CI value
eliminates complex selection hyperparameter tuning by simply retaining contexts
with positive CI values. To address practical challenges of label dependency and
computational overhead, we develop a parameterized surrogate model for CI value
prediction during inference. The model employs a hierarchical architecture that
captures both local query-context relevance and global inter-context interactions,
trained through oracle CI value supervision and end-to-end generator feedback.
Extensive experiments across 8 NLP tasks and multiple LLMs demonstrate that
our context selection method significantly outperforms state-of-the-art baselines,
effectively filtering poor-quality contexts while preserving cr...