AdaGReS: Redundancy-Aware Context Selection for RAG
Published:Dec 31, 2025 18:48
•1 min read
•ArXiv
Analysis
This paper addresses a critical issue in Retrieval-Augmented Generation (RAG): the inefficiency of standard top-k retrieval, which often includes redundant information. AdaGReS offers a novel solution by introducing a redundancy-aware context selection framework. This framework optimizes a set-level objective that balances relevance and redundancy, employing a greedy selection strategy under a token budget. The key innovation is the instance-adaptive calibration of the relevance-redundancy trade-off parameter, eliminating manual tuning. The paper's theoretical analysis provides guarantees for near-optimality, and experimental results demonstrate improved answer quality and robustness. This work is significant because it directly tackles the problem of token budget waste and improves the performance of RAG systems.
Key Takeaways
- •Addresses the problem of redundant context in RAG.
- •Proposes AdaGReS, a redundancy-aware context selection framework.
- •Employs a greedy selection strategy with a token budget.
- •Features instance-adaptive calibration to eliminate manual tuning.
- •Demonstrates improved answer quality and robustness in experiments.
Reference
“AdaGReS introduces a closed-form, instance-adaptive calibration of the relevance-redundancy trade-off parameter to eliminate manual tuning and adapt to candidate-pool statistics and budget limits.”