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        <title>Are Large Language Models Consistent over Value-laden Questions?</title>
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    <abstract>Large language models (LLMs) appear to bias their survey answers toward certain values. Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are they? To answer, we first define value consistency as the similarity of answers across 1) paraphrases of one question, 2) related questions under one topic, 3) multiple-choice and open-ended use-cases of one question, and 4) multilingual translations of a question to English, Chinese, German, and Japanese. We apply these measures to a few large, open LLMs including llama-3, as well as gpt-4o, using eight thousand questions spanning more than 300 topics. Unlike prior work, we find that models are relatively consistent across paraphrases, use-cases, translations, and within a topic. Still, some inconsistencies remain. Models are more consistent on uncontroversial topics (e.g., in the U.S., “Thanksgiving”) than on controversial ones (e.g. “euthanasia”). Base models are both more consistent compared to fine-tuned models and are uniform in their consistency across topics, while fine-tuned models are more inconsistent about some topics (e.g. “euthanasia”) than others (e.g. “Women’s rights”) like our human participants.</abstract>
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%0 Conference Proceedings
%T Are Large Language Models Consistent over Value-laden Questions?
%A Moore, Jared
%A Deshpande, Tanvi
%A Yang, Diyi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F moore-etal-2024-large
%X Large language models (LLMs) appear to bias their survey answers toward certain values. Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are they? To answer, we first define value consistency as the similarity of answers across 1) paraphrases of one question, 2) related questions under one topic, 3) multiple-choice and open-ended use-cases of one question, and 4) multilingual translations of a question to English, Chinese, German, and Japanese. We apply these measures to a few large, open LLMs including llama-3, as well as gpt-4o, using eight thousand questions spanning more than 300 topics. Unlike prior work, we find that models are relatively consistent across paraphrases, use-cases, translations, and within a topic. Still, some inconsistencies remain. Models are more consistent on uncontroversial topics (e.g., in the U.S., “Thanksgiving”) than on controversial ones (e.g. “euthanasia”). Base models are both more consistent compared to fine-tuned models and are uniform in their consistency across topics, while fine-tuned models are more inconsistent about some topics (e.g. “euthanasia”) than others (e.g. “Women’s rights”) like our human participants.
%U https://aclanthology.org/2024.findings-emnlp.891
%P 15185-15221

Markdown (Informal)

[Are Large Language Models Consistent over Value-laden Questions?](https://aclanthology.org/2024.findings-emnlp.891) (Moore et al., Findings 2024)

• Are Large Language Models Consistent over Value-laden Questions? (Moore et al., Findings 2024)

ACL

• Jared Moore, Tanvi Deshpande, and Diyi Yang. 2024. Are Large Language Models Consistent over Value-laden Questions?. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15185–15221, Miami, Florida, USA. Association for Computational Linguistics.