How Far Can We Extract Diverse Perspectives from Large Language Models?

Shirley Anugrah Hayati, Minhwa Lee, Dheeraj Rajagopal, Dongyeop Kang

Abstract
Collecting diverse human opinions is costly and challenging. This leads to a recent trend in exploiting large language models (LLMs) for generating diverse data for potential scalable and efficient solutions. However, the extent to which LLMs can generate diverse perspectives on subjective topics is still unclear. In this study, we explore LLMs’ capacity of generating diverse perspectives and rationales on subjective topics such as social norms and argumentative texts. We introduce the problem of extracting maximum diversity from LLMs. Motivated by how humans form opinions based on values, we propose a criteria-based prompting technique to ground diverse opinions. To see how far we can extract diverse perspectives from LLMs, or called diversity coverage, we employ a step-by-step recall prompting to generate more outputs from the model iteratively. Our methods, applied to various tasks, show that LLMs can indeed produce diverse opinions according to the degree of task subjectivity. We also find that LLMs performance of extracting maximum diversity is on par with human.
Anthology ID:
2024.emnlp-main.306
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5336–5366
Language:
URL:
https://aclanthology.org/2024.emnlp-main.306
DOI:
10.18653/v1/2024.emnlp-main.306
Bibkey:
Cite (ACL):
Shirley Anugrah Hayati, Minhwa Lee, Dheeraj Rajagopal, and Dongyeop Kang. 2024. How Far Can We Extract Diverse Perspectives from Large Language Models?. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5336–5366, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
How Far Can We Extract Diverse Perspectives from Large Language Models? (Hayati et al., EMNLP 2024)
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https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.306.pdf
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