解决RAG中冗余文本浪费token的问题，自动优化上下文选择。
AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG
- 基于相关性与冗余度权衡，动态筛选最优上下文片段。
- 在自然问题和生物药物数据集上，冗余减少30%以上，回答准确率提升。
- 无需调参，自适应调整策略，适合资源受限的生成任务。
检索增强生成（RAG）对上下文质量高度敏感，但传统top-k检索常返回冗余或近似重复的文本块，浪费令牌预算并降低生成效果。我们提出AdaGReS，一种面向令牌预算约束的冗余感知上下文选择框架，通过联合查询-文本相关性与集合内冗余惩罚的全局目标进行优化。AdaGReS在令牌预算限制下采用贪婪选择，利用目标函数导出的边际收益，并引入闭式、实例自适应的权衡参数校准机制，消除人工调参，适配候选池统计特征与预算上限。我们进一步提供理论分析，证明该目标在实际嵌入相似性条件下具备epsilon-近似子模性，为贪婪选择提供近优保证。在开放域问答（Natural Questions）与高冗余生物医学（药物）语料库上的实验表明，该方法在冗余控制与上下文质量方面持续提升，进而显著改善端到端答案质量与跨场景鲁棒性。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) is highly sensitive to the quality of selected context, yet standard top-k retrieval often returns redundant or near-duplicate chunks that waste token budget and degrade downstream generation. We present AdaGReS, a redundancy-aware context selection framework for token-budgeted RAG that optimizes a set-level objective combining query-chunk relevance and intra-set redundancy penalties. AdaGReS performs greedy selection under a token-budget constraint using marginal gains derived from the objective, and 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. We further provide a theoretical analysis showing that the proposed objective exhibits epsilon-approximate submodularity under practical embedding similarity conditions, yielding near-optimality guarantees for greedy selection. Experiments on open-domain question answering (Natural Questions) and a high-redundancy biomedical (drug) corpus demonstrate consistent improvements in redundancy control and context quality, translating to better end-to-end answer quality and robustness across settings.
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