Revealing Fine-Grained Values and Opinions in Large Language Models
Uncovering latent values and opinions in large language models (LLMs) can
help identify biases and mitigate potential harm. Recently, this has been
approached by presenting LLMs with survey questions and quantifying their
stances towards morally and politically charged statements. However, the
stances generated by LLMs can vary greatly depending on how they are prompted,
and there are many ways to argue for or against a given position. In this work,
we propose to address this by analysing a large and robust dataset of 156k LLM
responses to the 62 propositions of the Political Compass Test (PCT) generated
by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of
their generated stances and fine-grained analysis of the plain text
justifications for those stances. For fine-grained analysis, we propose to
identify tropes in the responses: semantically similar phrases that are
recurrent and consistent across different prompts, revealing patterns in the
text that a given LLM is prone to produce. We find that demographic features
added to prompts significantly affect outcomes on the PCT, reflecting bias, as
well as disparities between the results of tests when eliciting closed-form vs.
open domain responses. Additionally, patterns in the plain text rationales via
tropes show that similar justifications are repeatedly generated across models
and prompts even with disparate stances.
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Dustin Wright
claimed_verified
2024-07-03T13:44:57.789Z
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Dustin Wright
user
dwright37
667e6fb549c4138109a2488d
Arnav Arora
admin_assigned
2024-07-03T15:02:56.980Z
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Arnav Arora
user
ArnArora
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Nadav Borenstein
claimed_verified
2024-07-03T14:17:13.039Z
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Nadav Borenstein
user
Nadav
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Srishti Yadav
claimed_verified
2024-07-04T08:05:02.348Z
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Srishti
user
srishtiy
667e6fb549c4138109a24890
Serge Belongie
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Isabelle Augenstein
admin_assigned
2024-07-03T15:03:24.636Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1621507769190-608918b7df398c3b285ce960.jpeg
Isabelle Augenstein
user
IAugenstein
Revealing Fine-Grained Values and Opinions in Large Language Models
Uncovering latent values and opinions in large language models (LLMs) can
help identify biases and mitigate potential harm. Recently, this has been
approached by presenting LLMs with survey questions and quantifying their
stances towards morally and politically charged statements. However, the
stances generated by LLMs can vary greatly depending on how they are prompted,
and there are many ways to argue for or against a given position. In this work,
we propose to address this by analysing a large and robust dataset of 156k LLM
responses to the 62 propositions of the Political Compass Test (PCT) generated
by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of
their generated stances and fine-grained analysis of the plain text
justifications for those stances. For fine-grained analysis, we propose to
identify tropes in the responses: semantically similar phrases that are
recurrent and consistent across different prompts, revealing patterns in the
text that a given LLM is prone to produce. We find that demographic features
added to prompts significantly affect outcomes on the PCT, reflecting bias, as
well as disparities between the results of tests when eliciting closed-form vs.
open domain responses. Additionally, patterns in the plain text rationales via
tropes show that similar justifications are repeatedly generated across models
and prompts even with disparate stances.
Analysis of LLM responses to the Political Compass Test reveals bias through diverse prompt variations and recurring tropes in justifications.
large language models latent values opinions biases Political Compass Test prompt variations coarse-grained analysis fine-grained analysis tropes semantically similar phrases demographic features closed-form responses open domain responses