title: Revealing Fine-Grained Values and Opinions in Large Language Models
summary: 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.
paper_authors._id: 667e6fb549c4138109a2488c
paper_authors.name: Dustin Wright
paper_authors.status: claimed_verified
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paper_authors.fullname: Dustin Wright
paper_authors.type: user
paper_authors.user: dwright37
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paper_authors.name: Arnav Arora
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paper_authors.fullname: Arnav Arora
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paper_authors.user: ArnArora
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paper_authors.name: Nadav Borenstein
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paper_authors.fullname: Nadav Borenstein
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paper_authors.user: Nadav
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paper_authors.name: Srishti Yadav
paper_authors.status: claimed_verified
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paper_authors._id: 60c7ccd1dfbd57a384a0111e
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paper_authors.fullname: Srishti
paper_authors.type: user
paper_authors.user: srishtiy
paper_authors._id: 667e6fb549c4138109a24890
paper_authors.name: Serge Belongie
paper_authors._id: 667e6fb549c4138109a24891
paper_authors.name: Isabelle Augenstein
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-03T15:03:24.636Z
paper_authors._id: 608918b7df398c3b285ce960
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1621507769190-608918b7df398c3b285ce960.jpeg
paper_authors.fullname: Isabelle Augenstein
paper_authors.type: user
paper_authors.user: IAugenstein
paper_title: Revealing Fine-Grained Values and Opinions in Large Language Models
paper_summary: 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.
paper_ai_summary: Analysis of LLM responses to the Political Compass Test reveals bias through diverse prompt variations and recurring tropes in justifications.
paper_ai_keywords: 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