arXiv 论文提出基于 Schwartz 价值理论的 LLM 辩论文本评估框架
Value-Rich, Differently Organized: Measuring Value Expression and Organization in AI-Generated Debates
几个生成系统的辩论里价值观点很多，但跟人比撒得更散，论文给了套基于 Schwartz 理论的量化方法，代码开源。
一篇 arXiv 论文（编号 2610.28604）提出诊断性指标框架，比较人类参考辩论与 LLM 生成辩论中价值观的表达与组织方式。框架基于 Schwartz 基本价值理论（Basic-10），在话语、立场和整场辩论三个层级构建价值分布，度量价值覆盖、对齐、扩散和跨轮动态。实验发现 AI 生成的辩论激活的价值维度与人类参考相当甚至更多，但各轮话语中价值更局部扩散，组织方式与人类不同。代码已在 GitHub 开源。
Value-Rich, Differently Organized: Measuring Value Expression and Organization in AI-Generated Debates
Current evaluation of LLM-generated debate largely focuses on argumentative quality, leaving the value structure of generated arguments underexamined. We introduce a diagnostic metric framework for comparing how values are expressed and organized in human-reference and AI-generated debates. The framework separates value expression, the range of recognizable values a debate invokes, from value organization, how those values are distributed and sustained across turns. Grounded in Schwartz's Basic-10 value theory, it represents debates as multi-level value distributions over argumentative units, utterances, stances, and full debates, enabling metrics for value coverage, alignment, diffusion, and cross-turn dynamics. Across multiple generation systems, AI-generated debates are broadly value-expressive, often activating as many or more value dimensions than matched human references, but they organize these values differently. Their utterances are more locally value-diffuse, and source-blind judges distinguish value clarity from value focus. These findings show that debate evaluation should consider not only value presence, but also value organization. Code and supplementary materials are available at https://github.com/Maoyuan-li/Value-Expression-and-Organization.
