用探针提示法将大模型推理路径压缩为可理解的语义节点
Automated Attribution Graph Interpretation via Probe Prompting
- 基于少量概念探针提示，将归因图特征聚类为语义对齐的超节点
- 在45,596次实体替换干预中，所有超节点均表现出预期的可控转向行为
- 提供可复用工具链，支持通过因果干预验证标签准确性
尽管我们清楚大型语言模型（LLM）从输入到输出的精确计算过程，但这些计算仍极难解释。一种简化理解的方法是构建稀疏的计算图，以最少节点捕捉模型主要行为。跨层译码器（CLT）虽能分解MLP的密集计算，但即便对于短提示，生成的电路仍含数千个节点。现有自动化解释方法通过语料激活标记单个特征，常缺乏因果干预验证。本文提出探针提示（probe prompting），一种透明的规则化流程：通过少量概念靶向探针提示，根据其响应将归因图特征聚类为与概念对齐的超节点，结果以跨提示激活签名（CPAS）总结。在四个事实领域，使用Gemma-2-2B和公开的CLT词典，在45,596次实体替换干预下，所有标注超节点均表现出预测的控制行为。代码、数据集及交互式演示已匿名发布，形成可复用的校准框架，用于通过因果干预验证超节点标签。
原文摘要 · Abstract (English)
Even though we know the precise computations that lead from a large language model (LLM) input to its output this computation remains very hard to interpret. One way to make it easier to understand this process is by creating a sparse computational graph that captures most of the model behavior with smallest number of computational nodes. Cross-layer transcoders (CLT) decompose the dense computations of the MLP but the resulting circuits still contain thousands of nodes even for short prompts. Existing automated interpretation methods label individual features from corpus activations, and it often happens that these labels are not validated by causal intervention. We introduce probe prompting, a transparent rule-based pipeline that groups the features of an attribution graph into concept-aligned supernodes from their responses on a small set of concept-targeted probe prompts, summarized as Cross-Prompt Activation Signatures (CPAS). Across four factual domains, on Gemma-2-2B with a public CLT dictionary and 45,596 entity-swap interventions, we find that the labeled supernodes have the predicted steering behavior in every one of them. Code, datasets, and an interactive demo are released anonymously as a reusable harness for calibrating supernode labels against causal interventions.
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