@inproceedings{kamal-etal-2025-detailed,
title = "A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models",
author = "Kamal, Sadia and
Prakash, Lalu Prasad Yadav and
Rafiuddin, S M and
Rakib, Mohammed and
Sen, Atriya and
Choudhury, Sagnik Ray",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-short.25/",
pages = "284--303",
ISBN = "979-8-89176-299-2",
abstract = "The Political Compass Test (PCT) and similar surveys are commonly used to assess political bias in auto-regressive LLMs. Our rigorous statistical experiments show that while changes to standard generation parameters have minimal effect on PCT scores, prompt phrasing and fine-tuning individually and together can significantly influence results. Interestingly, fine-tuning on politically rich vs. neutral datasets does not lead to different shifts in scores. We also generalize these findings to a similar popular test called 8 Values. Humans do not change their responses to questions when prompted differently ({``}answer this question'' vs ``state your opinion''), or after exposure to politically neutral text, such as mathematical formulae. But the fact that the models do so raises concerns about the validity of these tests for measuring model bias, and paves the way for deeper exploration into how political and social views are encoded in LLMs."
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%0 Conference Proceedings
%T A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models
%A Kamal, Sadia
%A Prakash, Lalu Prasad Yadav
%A Rafiuddin, S. M.
%A Rakib, Mohammed
%A Sen, Atriya
%A Choudhury, Sagnik Ray
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-299-2
%F kamal-etal-2025-detailed
%X The Political Compass Test (PCT) and similar surveys are commonly used to assess political bias in auto-regressive LLMs. Our rigorous statistical experiments show that while changes to standard generation parameters have minimal effect on PCT scores, prompt phrasing and fine-tuning individually and together can significantly influence results. Interestingly, fine-tuning on politically rich vs. neutral datasets does not lead to different shifts in scores. We also generalize these findings to a similar popular test called 8 Values. Humans do not change their responses to questions when prompted differently (“answer this question” vs “state your opinion”), or after exposure to politically neutral text, such as mathematical formulae. But the fact that the models do so raises concerns about the validity of these tests for measuring model bias, and paves the way for deeper exploration into how political and social views are encoded in LLMs.
%U https://aclanthology.org/2025.ijcnlp-short.25/
%P 284-303
Markdown (Informal)
[A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models](https://aclanthology.org/2025.ijcnlp-short.25/) (Kamal et al., IJCNLP-AACL 2025)
ACL
- Sadia Kamal, Lalu Prasad Yadav Prakash, S M Rafiuddin, Mohammed Rakib, Atriya Sen, and Sagnik Ray Choudhury. 2025. A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 284–303, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.