Robust Explanations for User Trust in Enterprise NLP Systems
Summary: arXiv:2604.12069v1 Announce Type: cross
Abstract: Robust explanations are increasingly required for user trust in enterprise NLP, yet pre-deployment validation is difficult in the common case of black-box deployment (API-only access) where representation-based explainers are infeasible and existing studies provide limited guidance on whether explanations remain stable under real user noise, especially when organizations migrate from encoder classifiers to decoder LLMs. To close this gap, we propose a unified black-box robustness evaluation framework for token-level explanations based on leave-one-out occlusion, and operationalize explanation robustness with top-token flip rate under realistic perturbations (swap, deletion, shuffling, and back-translation) at multiple severity levels.
Introduction
The rise of Natural Language Processing (NLP) systems in enterprise applications has brought about a significant need for transparency and trust. As organizations increasingly rely on these systems for decision-making, understanding how they generate outputs becomes crucial. This article discusses a recent study that aims to enhance user trust in enterprise NLP through robust explanations.
Challenges in Black-Box Deployments
In many cases, enterprise NLP models are deployed in a black-box manner, where users interact with an API without visibility into the underlying mechanisms. This lack of transparency presents several challenges:
- Difficulty in validating model performance pre-deployment.
- Limited guidance on explanation stability amidst user noise.
- Uncertainties when transitioning from encoder classifiers to decoder-based large language models (LLMs).
Proposed Evaluation Framework
The authors propose a comprehensive framework for evaluating the robustness of token-level explanations. This framework utilizes a leave-one-out occlusion method, which assesses how much the removal of a token affects the model’s output.
To operationalize explanation robustness, the study introduces the top-token flip rate, which measures the frequency of changes in the most influential tokens under various perturbations. These perturbations include:
- Swap
- Deletion
- Shuffling
- Back-translation
The evaluation is conducted at multiple severity levels to provide a comprehensive understanding of robustness.
Findings from the Study
The study involved a systematic comparison across three benchmark datasets and six models, spanning both encoder and decoder families including BERT, RoBERTa, Qwen 7B/14B, and Llama 8B/70B, totaling 64,800 cases. Key findings include:
- Decoder LLMs display substantially more stable explanations than encoder baselines, with an average of 73% lower flip rates.
- Stability improves with model scale, showing a 44% gain from 7B to 70B models.
Practical Implications
The results not only demonstrate the importance of model selection but also emphasize the need for explanation robustness in compliance-sensitive applications. The authors present a cost-robustness tradeoff curve, assisting organizations in selecting models and explanations that fit their operational needs while ensuring user trust.
Conclusion
As enterprise NLP systems become integral to decision-making processes, ensuring user trust through robust explanations is paramount. This study lays the groundwork for further research and development in creating more transparent and reliable NLP applications.