Although small Large Language models (sLLMs) have been widely deployed in practical applications, little attention has been paid to their value-reasoning abilities, particularly in terms of reasoning reliability. To address this gap, we propose a systematic evaluation framework for assessing the Value-Reasoning Reliability of sLLMs. We define Value-Reasoning Reliability as comprising: (1) Output consistency under identical prompts, (2) Output Robustness under semantically equivalent prompts, (3) Maintaining stable value reasoning in the face of attacks, and (4) Consistency of value reasoning in open-ended value expression tasks. Our framework includes three core tasks: Repetition Consistency task, Interaction Stability task, and Open-ended Expression Consistency task. We further incorporate self-reported confidence scores to evaluate the model’s value reasoning reliability from two perspectives: the model’s self-awareness of its values, and its value-based decision-making. Our findings show that models vary significantly in their stability when responding to value-related questions. Moreover, we observe considerable output randomness, which is not always correlated with the self-reported confidence or expressed value preferences. This suggests that current models lack a reliable internal mechanism for stable value reasoning when addressing value-sensitive queries.

“中西方谚语是中西方文化的结晶,分别蕴含着中西方文化中最基本的价值观。但目前缺乏中西方谚语价值观资源,难以对谚语所体现的中西方价值观进行全面的研究,特别是定量对比研究。因此本文设计了多元价值观体系,包含动机及需求、共同及特色价值观、价值判断和使用场景,根据这个体系构建了中西方谚语多元价值观资源库并进行了考察与对比分析。本文发现中西谚语在价值判断、使用场景及部分价值观上具有相似性,在具体内涵表达上各具独特性。”