Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation

ArXi:2503.16771v3 Announce Type: replace-cross As Large Language Models for Code (LM4Code) become integral to software engineering, establishing trust in their output becomes critical. However, standard accuracy metrics obscure the underlying reasoning of generative models, offering little insight into how decisions are made. Although post-hoc interpretability methods attempt to fill this gap, they often restrict explanations to local, token-level insights, which fail to provide a developer-understandable global analysis.