@inproceedings{tang-etal-2026-probing,
title = "Probing the Plasticity and Correlation of {LLM} Value Systems: {LLM} Value Rankings are Not Stable",
author = "Tang, Zhenheng and
Pan, Qihua and
Shen, Jingya and
Liu, Xiang and
Wang, Qian and
Li, Bo and
Chu, Xiaowen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1715/",
pages = "34310--34345",
ISBN = "979-8-89176-395-1",
abstract = "The value alignment of Large Language Models (LLMs) is critical because value is the foundation of LLM decision-making and behavior. Some recent work show that LLMs have similar value rankings. However, little is known about how susceptible LLM value rankings are to external influence and how different values are correlated with each other. In this work, we investigate the plasticity of LLM value systems by examining how their value rankings are influenced by different prompting strategies and exploring the intrinsic relationships between values. To this end, we design 6 different value transformation prompting methods including direct instruction, rubrics, in-context learning, scenario, persuasion, and persona, and benchmark the effectiveness of these methods on 3 different families and totally 8 LLMs. Our main findings include that the value rankings in large LLMs are much more susceptible to external influence than small LLMs, and there are intrinsic correlations between certain values (e.g., Privacy and Respect). Besides, through detailed correlation analysis, we find that the value correlations are more similar between large LLMs of different families than small LLMs of the same family. We also identify that scenario method is the strongest persuader and can help entrench the value rankings."
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<abstract>The value alignment of Large Language Models (LLMs) is critical because value is the foundation of LLM decision-making and behavior. Some recent work show that LLMs have similar value rankings. However, little is known about how susceptible LLM value rankings are to external influence and how different values are correlated with each other. In this work, we investigate the plasticity of LLM value systems by examining how their value rankings are influenced by different prompting strategies and exploring the intrinsic relationships between values. To this end, we design 6 different value transformation prompting methods including direct instruction, rubrics, in-context learning, scenario, persuasion, and persona, and benchmark the effectiveness of these methods on 3 different families and totally 8 LLMs. Our main findings include that the value rankings in large LLMs are much more susceptible to external influence than small LLMs, and there are intrinsic correlations between certain values (e.g., Privacy and Respect). Besides, through detailed correlation analysis, we find that the value correlations are more similar between large LLMs of different families than small LLMs of the same family. We also identify that scenario method is the strongest persuader and can help entrench the value rankings.</abstract>
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%0 Conference Proceedings
%T Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable
%A Tang, Zhenheng
%A Pan, Qihua
%A Shen, Jingya
%A Liu, Xiang
%A Wang, Qian
%A Li, Bo
%A Chu, Xiaowen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tang-etal-2026-probing
%X The value alignment of Large Language Models (LLMs) is critical because value is the foundation of LLM decision-making and behavior. Some recent work show that LLMs have similar value rankings. However, little is known about how susceptible LLM value rankings are to external influence and how different values are correlated with each other. In this work, we investigate the plasticity of LLM value systems by examining how their value rankings are influenced by different prompting strategies and exploring the intrinsic relationships between values. To this end, we design 6 different value transformation prompting methods including direct instruction, rubrics, in-context learning, scenario, persuasion, and persona, and benchmark the effectiveness of these methods on 3 different families and totally 8 LLMs. Our main findings include that the value rankings in large LLMs are much more susceptible to external influence than small LLMs, and there are intrinsic correlations between certain values (e.g., Privacy and Respect). Besides, through detailed correlation analysis, we find that the value correlations are more similar between large LLMs of different families than small LLMs of the same family. We also identify that scenario method is the strongest persuader and can help entrench the value rankings.
%U https://aclanthology.org/2026.findings-acl.1715/
%P 34310-34345
Markdown (Informal)
[Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable](https://aclanthology.org/2026.findings-acl.1715/) (Tang et al., Findings 2026)
ACL
- Zhenheng Tang, Qihua Pan, Jingya Shen, Xiang Liu, Qian Wang, Bo Li, and Xiaowen Chu. 2026. Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable. In Findings of the Association for Computational Linguistics: ACL 2026, pages 34310–34345, San Diego, California, United States. Association for Computational Linguistics.