语言模型常自相矛盾，这篇论文分析了问题根源与解决方向。
Consistency in Language Models: Current Landscape, Challenges, and Future Directions
- 梳理当前语言模型一致性研究的现状与方法
- 指出现有模型在任务和领域间表现不一致
- 呼吁建立高质量评估基准和跨学科解决方案
有效语言使用的核心在于一致性：在相似语境中表达相似含义，避免自相矛盾。尽管人类交流自然具备这一特性，当前最先进的语言模型在特定任务和领域应用中难以保持可靠的一致性。本文系统梳理了语言模型一致性研究的现状，分析了现有的测量方法，并识别出关键研究空白。研究发现亟需高质量的评估基准来衡量一致性，同时需要跨学科方法，在保持模型实用性的同时确保其一致性。
原文摘要 · Abstract (English)
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.
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