@inproceedings{sakhawat-etal-2026-llm,
title = "{LLM}-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of {LLM}s as a Context-Conditioned Distribution",
author = "Sakhawat, Adib and
Raiyan, Syed Rifat and
Islam, Tahsin and
Farhin, Takia and
Mahmud, Hasan and
Hasan, Md Kamrul",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.145/",
pages = "3167--3209",
ISBN = "979-8-89176-439-2",
abstract = "We argue, with systematic empirical evidence, that a large language model{'}s political ideology is not a fixed point, but a *conditional distribution* $\mathbb{P}($position$\mid$context$)$ over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA{--}CHES projection models, which map responses onto three validated dimensions (*lrgen*, *lrecon*, *galtan*) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under-represented languages displace coordinates by up to 0.57 and 0.52 units, respectively, while chain-of-thought reasoning often amplifies rather than dampens paraphrase instability. Despite this local plasticity, the model cohort occupies a remarkably narrow Overton envelope overall, occupying roughly one-third the spread of major European parties. Supported by a multi-trait multi-method (MTMM) analysis, we conclude that a single point cannot summarize LLM political behavior; it must be characterized as a shape. Our code and data are publicly available."
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<abstract>We argue, with systematic empirical evidence, that a large language model’s political ideology is not a fixed point, but a *conditional distribution* \mathbbP(position\midcontext) over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA–CHES projection models, which map responses onto three validated dimensions (*lrgen*, *lrecon*, *galtan*) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under-represented languages displace coordinates by up to 0.57 and 0.52 units, respectively, while chain-of-thought reasoning often amplifies rather than dampens paraphrase instability. Despite this local plasticity, the model cohort occupies a remarkably narrow Overton envelope overall, occupying roughly one-third the spread of major European parties. Supported by a multi-trait multi-method (MTMM) analysis, we conclude that a single point cannot summarize LLM political behavior; it must be characterized as a shape. Our code and data are publicly available.</abstract>
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%0 Conference Proceedings
%T LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution
%A Sakhawat, Adib
%A Raiyan, Syed Rifat
%A Islam, Tahsin
%A Farhin, Takia
%A Mahmud, Hasan
%A Hasan, Md Kamrul
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F sakhawat-etal-2026-llm
%X We argue, with systematic empirical evidence, that a large language model’s political ideology is not a fixed point, but a *conditional distribution* \mathbbP(position\midcontext) over a real political space. We evaluate nine current LLMs using a unified measurement framework anchored by VAA–CHES projection models, which map responses onto three validated dimensions (*lrgen*, *lrecon*, *galtan*) across six contextual axes. Our findings reveal high sensitivity to context: persuasive framing and under-represented languages displace coordinates by up to 0.57 and 0.52 units, respectively, while chain-of-thought reasoning often amplifies rather than dampens paraphrase instability. Despite this local plasticity, the model cohort occupies a remarkably narrow Overton envelope overall, occupying roughly one-third the spread of major European parties. Supported by a multi-trait multi-method (MTMM) analysis, we conclude that a single point cannot summarize LLM political behavior; it must be characterized as a shape. Our code and data are publicly available.
%U https://aclanthology.org/2026.aacl-long.145/
%P 3167-3209
Markdown (Informal)
[LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution](https://aclanthology.org/2026.aacl-long.145/) (Sakhawat et al., AACL-IJCNLP 2026)
ACL