@inproceedings{ma-etal-2024-potential,
title = "The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models",
author = "Ma, Bolei and
Wang, Xinpeng and
Hu, Tiancheng and
Haensch, Anna-Carolina and
Hedderich, Michael A. and
Plank, Barbara and
Kreuter, Frauke",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.513",
doi = "10.18653/v1/2024.findings-emnlp.513",
pages = "8783--8805",
abstract = "Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. These cognitive-behavioral traits include typically Attitudes, Opinions, Values (AOVs). However, measuring AOVs embedded within LLMs remains opaque, and different evaluation methods may yield different results. This has led to a lack of clarity on how different studies are related to each other and how they can be interpreted. This paper aims to bridge this gap by providing a comprehensive overview of recent works on the evaluation of AOVs in LLMs. Moreover, we survey related approaches in different stages of the evaluation pipeline in these works. By doing so, we address the potential and challenges with respect to understanding the model, human-AI alignment, and downstream application in social sciences. Finally, we provide practical insights into evaluation methods, model enhancement, and interdisciplinary collaboration, thereby contributing to the evolving landscape of evaluating AOVs in LLMs.",
}
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%0 Conference Proceedings
%T The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models
%A Ma, Bolei
%A Wang, Xinpeng
%A Hu, Tiancheng
%A Haensch, Anna-Carolina
%A Hedderich, Michael A.
%A Plank, Barbara
%A Kreuter, Frauke
%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 ma-etal-2024-potential
%X Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. These cognitive-behavioral traits include typically Attitudes, Opinions, Values (AOVs). However, measuring AOVs embedded within LLMs remains opaque, and different evaluation methods may yield different results. This has led to a lack of clarity on how different studies are related to each other and how they can be interpreted. This paper aims to bridge this gap by providing a comprehensive overview of recent works on the evaluation of AOVs in LLMs. Moreover, we survey related approaches in different stages of the evaluation pipeline in these works. By doing so, we address the potential and challenges with respect to understanding the model, human-AI alignment, and downstream application in social sciences. Finally, we provide practical insights into evaluation methods, model enhancement, and interdisciplinary collaboration, thereby contributing to the evolving landscape of evaluating AOVs in LLMs.
%R 10.18653/v1/2024.findings-emnlp.513
%U https://aclanthology.org/2024.findings-emnlp.513
%U https://doi.org/10.18653/v1/2024.findings-emnlp.513
%P 8783-8805
Markdown (Informal)
[The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models](https://aclanthology.org/2024.findings-emnlp.513) (Ma et al., Findings 2024)
ACL
- Bolei Ma, Xinpeng Wang, Tiancheng Hu, Anna-Carolina Haensch, Michael A. Hedderich, Barbara Plank, and Frauke Kreuter. 2024. The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8783–8805, Miami, Florida, USA. Association for Computational Linguistics.