Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes

Abstract:The widespread adoption of proprietary Large Language Models (LLMs) accessed strictly through closed APIs has created a critical challenge for responsible deployment: a fundamental lack of interpretability. To address this, we propose a model-agnostic, post-hoc attribution interpreter operating at the sentence level. Our approach trains an Energy-Based Model (EBM) as a surrogate to capture the LLM's internal conceptual consistency between prompts and responses. This energy landscape guides the training of a lightweight interpreter network. Uniquely, our interpreter operates as a standalone tool; once trained, it quantifies the influence of prompt sentences on a user-specified target output without requiring further API queries to the LLM. By globally training a local interpreter across diverse inputs, our framework captures broader generation patterns and mitigates instance-specific biases. Experiments demonstrate that our EBM accurately simulates the target LLM, allowing the interpreter to effectively identify the prompt sentences most influential in generating specific target outputs.

Artificial Intelligence (cs.AI)

arXiv:2608.02879 [cs.AI]

(or arXiv:2608.02879v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.02879 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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