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        <title>Will LLMs Sink or Swim? Exploring Decision-Making Under Pressure</title>
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    <abstract>Recent advancements in Large Language Models (LLMs) have demonstrated their ability to simulate human-like decision-making, yet the impact of psychological pressures on their decision-making processes remains underexplored. To understand how psychological pressures influence decision-making in LLMs, we tested LLMs on various high-level tasks, using both explicit and implicit pressure prompts. Moreover, we examined LLM responses under different personas to compare with human behavior under pressure. Our findings show that pressures significantly affect LLMs’ decision-making, varying across tasks and models. Persona-based analysis suggests some models exhibit human-like sensitivity to pressure, though with some variability. Furthermore, by analyzing both the responses and reasoning patterns, we identified the values LLMs prioritize under specific social pressures. These insights deepen our understanding of LLM behavior and demonstrate the potential for more realistic social simulation experiments.</abstract>
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%0 Conference Proceedings
%T Will LLMs Sink or Swim? Exploring Decision-Making Under Pressure
%A Kim, Kyusik
%A Jeon, Hyeonseok
%A Ryu, Jeongwoo
%A Suh, Bongwon
%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 kim-etal-2024-will
%X Recent advancements in Large Language Models (LLMs) have demonstrated their ability to simulate human-like decision-making, yet the impact of psychological pressures on their decision-making processes remains underexplored. To understand how psychological pressures influence decision-making in LLMs, we tested LLMs on various high-level tasks, using both explicit and implicit pressure prompts. Moreover, we examined LLM responses under different personas to compare with human behavior under pressure. Our findings show that pressures significantly affect LLMs’ decision-making, varying across tasks and models. Persona-based analysis suggests some models exhibit human-like sensitivity to pressure, though with some variability. Furthermore, by analyzing both the responses and reasoning patterns, we identified the values LLMs prioritize under specific social pressures. These insights deepen our understanding of LLM behavior and demonstrate the potential for more realistic social simulation experiments.
%U https://aclanthology.org/2024.findings-emnlp.668
%P 11425-11450

Markdown (Informal)

[Will LLMs Sink or Swim? Exploring Decision-Making Under Pressure](https://aclanthology.org/2024.findings-emnlp.668) (Kim et al., Findings 2024)

• Will LLMs Sink or Swim? Exploring Decision-Making Under Pressure (Kim et al., Findings 2024)

ACL

• Kyusik Kim, Hyeonseok Jeon, Jeongwoo Ryu, and Bongwon Suh. 2024. Will LLMs Sink or Swim? Exploring Decision-Making Under Pressure. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11425–11450, Miami, Florida, USA. Association for Computational Linguistics.