解决大模型长文本解释中的特征稀释问题，实现精准定位。
Focus-LIME: Surgical Interpretation of Long-Context Large Language Models via Proxy-Based Neighborhood Selection
- 用代理模型筛选关键上下文区域，缩小扰动范围
- 在优化后上下文中实现细粒度特征归因，解释更精准
- 适合法律审计、代码调试等高风险场景的可解释性需求
随着大语言模型（LLMs）处理超长上下文的能力不断增强，实现特征级别的精确解释对法律审计、代码调试等高风险任务至关重要。然而，现有局部模型无关解释方法面临严峻困境：基于特征的方法因特征维度过高导致归因稀释，难以提供真实可信的解释。本文提出 Focus-LIME，一种由粗到精的框架，通过代理模型构建扰动邻域，使目标模型仅在优化后的上下文中进行细粒度归因。在长上下文基准上的实证评估表明，该方法使精细解释成为可能，并为用户提供忠实可靠的解释。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) scale to handle massive context windows, achieving surgical feature-level interpretation is essential for high-stakes tasks like legal auditing and code debugging. However, existing local model-agnostic explanation methods face a critical dilemma in these scenarios: feature-based methods suffer from attribution dilution due to high feature dimensionality, thus failing to provide faithful explanations. In this paper, we propose Focus-LIME, a coarse-to-fine framework designed to restore the tractability of surgical interpretation. Focus-LIME utilizes a proxy model to curate the perturbation neighborhood, allowing the target model to perform fine-grained attribution exclusively within the optimized context. Empirical evaluations on long-context benchmarks demonstrate that our method makes surgical explanations practicable and provides faithful explanations to users.
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