Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-Off

TL;DR

This paper demonstrates that regulating large language models to use human-defined features causes a performance trade-off but can enhance human-AI collaboration efficiency and confidence in practical applications.

Contribution

It introduces a method to build regulatable LLMs and quantifies the impact of regulation constraints on performance and human collaboration.

Findings

Regulation causes a 7.34% drop in classification accuracy.

Regulated models improve human task speed.

Regulated models increase appropriate confidence in AI assistance.

Abstract

Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantly, how it would effect model performance alongside human collaboration if actually realized. In this paper, we attempt to answer these questions by building a regulatable large-language model (LLM), and then quantifying how the additional constraints involved affect (1) model performance, alongside (2) human collaboration. Our empirical results reveal that it is possible to force an LLM to use human-defined features in a transparent way, but a "regulation performance trade-off" previously not considered reveals itself in the form of a 7.34% classification performance drop. Surprisingly however, we show that despite this, such systems actually improve human task performance speed and appropriate confidence in a realistic deployment setting compared to no AI assistance, thus paving a way for fair, regulatable AI, which benefits users.