Consistency in Language Models: Current Landscape, Challenges, and Future Directions

Jekaterina Novikova, Carol Myrick Anderson, Borhane Blili-Hamelin, Domenic Rosati, Subhabrata Majumdar

Keywords: Language model consistency, language models, robustness

TL;DR: We present a call to action for the AI community to work on the underexplored area of consistency in language models.

Abstract: The hallmark of effective language use lies in consistency: expressing similar meanings in similar contexts and avoiding contradictions. While human communication naturally demonstrates this principle, state-of-the-art language models (LMs) struggle to maintain reliable consistency across task- and domain-specific applications. Here we examine the landscape of consistency research in LMs, analyze current approaches to measure aspects of consistency, and identify critical research gaps. Our findings point to an urgent need for quality benchmarks to measure and interdisciplinary approaches to ensure consistency while preserving utility.

Submission Number: 6