Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), but their lack of interpretability has been a major concern. Current methods for interpreting LLMs are post hoc, applied after inference time, and have limitations such as their focus on low-level features and lack of explainability at higher level text units. In this work, we introduce proto-lm, a prototypical network-based white-box framework that allows LLMs to learn immediately interpretable embeddings during the fine-tuning stage while maintaining competitive performance. Our method's applicability and interpretability are demonstrated through experiments on a wide range of NLP tasks, and our results indicate a new possibility of creating interpretable models without sacrificing performance. This novel approach to interpretability in LLMs can pave the way for more interpretable models without the need to sacrifice performance.

翻译：大语言模型（LLMs）显著推动了自然语言处理（NLP）领域的发展，但其缺乏可解释性一直是一个主要问题。当前解释LLMs的方法多为事后型（post hoc），在推理后应用，且存在局限性，例如关注低层特征以及对高层级文本单元缺乏可解释性。在本研究中，我们提出Proto-lm，一种基于原型网络的白盒框架，使LLMs在微调阶段能够学习到即时可解释的嵌入，同时保持竞争性性能。通过一系列NLP任务的实验，我们验证了该方法的应用性和可解释性，而研究结果表明，在不牺牲性能的前提下构建可解释模型成为可能。这种针对LLMs可解释性的创新方法，为无需牺牲性能即可实现更可解释模型开辟了新的可能性。