Robust Explanations for User Trust in Enterprise NLP Systems

This research proposes a unified black-box robustness evaluation framework for token-level explanations to improve user trust in enterprise NLP systems, especially when migrating to LLMs. It operationalizes robustness via top-token flip rate under realistic perturbations, conducting a systematic comparison across various encoder and decoder architectures like BERT, RoBERTa, Qwen, and Llama.