Computer Science > Computation and Language
[Submitted on 21 Mar 2024 (v1), last revised 23 Jul 2025 (this version, v2)]
Title:Multi-Level Explanations for Generative Language Models
View PDF HTML (experimental)Abstract:Despite the increasing use of large language models (LLMs) for context-grounded tasks like summarization and question-answering, understanding what makes an LLM produce a certain response is challenging. We propose Multi-Level Explanations for Generative Language Models (MExGen), a technique to provide explanations for context-grounded text generation. MExGen assigns scores to parts of the context to quantify their influence on the model's output. It extends attribution methods like LIME and SHAP to LLMs used in context-grounded tasks where (1) inference cost is high, (2) input text is long, and (3) the output is text. We conduct a systematic evaluation, both automated and human, of perturbation-based attribution methods for summarization and question answering. The results show that our framework can provide more faithful explanations of generated output than available alternatives, including LLM self-explanations. We open-source code for MExGen as part of the ICX360 toolkit: https://github$.$com/IBM/ICX360.
Submission history
From: Lucas Monteiro Paes [view email][v1] Thu, 21 Mar 2024 15:06:14 UTC (3,545 KB)
[v2] Wed, 23 Jul 2025 15:48:23 UTC (2,003 KB)
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