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        <title>Investigating Value-Reasoning Reliability in Small Large Language Models</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Investigating Value-Reasoning Reliability in Small Large Language Models
%A Du, Xia
%A Sun, Shuhan
%A Liu, Pengyuan
%A Yu, Dong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F du-etal-2025-investigating
%X 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.
%U https://aclanthology.org/2025.emnlp-main.395/
%P 7757-7797

Markdown (Informal)

[Investigating Value-Reasoning Reliability in Small Large Language Models](https://aclanthology.org/2025.emnlp-main.395/) (Du et al., EMNLP 2025)

• Investigating Value-Reasoning Reliability in Small Large Language Models (Du et al., EMNLP 2025)

ACL

• Xia Du, Shuhan Sun, Pengyuan Liu, and Dong Yu. 2025. Investigating Value-Reasoning Reliability in Small Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 7757–7797, Suzhou, China. Association for Computational Linguistics.