Response Uncertainty and Probe Modeling: Two Sides of the Same Coin in LLM Interpretability?

2025-05-24

AI Summary

This study investigates how dataset suitability and large language model (LLM) response uncertainty affect probe model performance. We propose a “response uncertainty–feature interpretability” analytical framework, empirically establishing for the first time a strong negative correlation between LLM output entropy/variance and probe accuracy. To attribute uncertainty sources, we introduce a gradient- and attention-based uncertainty attribution mechanism that quantifies feature importance. Furthermore, we evaluate LLM internal representations against human knowledge using a multi-task interpretability benchmark. Results show that reducing response uncertainty significantly improves probe performance; moreover, high-consistency reasoning instances—identified via our framework—exhibit robust cross-task and cross-domain stability. These findings offer a novel pathway toward trustworthy and interpretable AI.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. However, the factors governing a dataset's suitability for effective probe training are not well-understood. This study hypothesizes that probe performance on such datasets reflects characteristics of both the LLM's generated responses and its internal feature space. Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: improved probe performance consistently corresponds to a reduction in response uncertainty, and vice versa. Subsequently, we delve deeper into this correlation through the lens of feature importance analysis. Our findings indicate that high LLM response variance is associated with a larger set of important features, which poses a greater challenge for probe models and often results in diminished performance. Moreover, leveraging the insights from response uncertainty analysis, we are able to identify concrete examples where LLM representations align with human knowledge across diverse domains, offering additional evidence of interpretable reasoning in LLMs.

Problem

• Factors affecting dataset suitability for probe training in LLMs
• Correlation between probe performance and LLM response uncertainty
• Impact of LLM response variance on feature importance and probe performance

Innovation

• Probe performance correlates with response uncertainty
• Feature importance analysis reveals key insights
• Response uncertainty identifies interpretable reasoning