Proto-lm: A Prototypical Network-Based Framework for Built-in Interpretability in Large Language Models
This addresses the problem of interpretability for users of LLMs, offering a built-in solution that is incremental by integrating prototypical networks into existing fine-tuning processes.
The paper tackles the lack of interpretability in Large Language Models by introducing proto-lm, a prototypical network-based framework that enables LLMs to learn interpretable embeddings during fine-tuning while maintaining competitive performance, as demonstrated across various NLP tasks.
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.