[2T5-OS-5b] OS-5

[2T5-OS-5b-02] Mitigating Cognitive Biases in Large Language Models

〇Yasuaki Sumita¹, Koh Takeuchi¹, Hisashi Kashima¹ (1. Kyoto University)

Keywords:Large Language Model, Cognitive Bias

In recent years, Large Language Models (LLMs) have been developed and have shown high performance on various tasks. This high level of performance is achieved by learning from large corpora of documents written by humans. However, since humans are subject to various cognitive biases, leading to irrational judgments, LLMs can also be influenced by these cognitive biases resulting in irrational decision-making. For example, changing the order of options in multiple-choice questions affects the performance of LLMs due to order bias. Our research aims to mitigate such cognitive biases and prompt LLMs to make rational decisions. In our proposed methods, we apply cognitive biases mitigation methods used in crowdsourcing to prompts that are input to LLMs. To test the effectiveness of our methods, we conduct experiments on GPT-3.5 and GPT-4 to evaluate the influence of six cognitive biases on the outputs before and after applying our methods. The results showed that our proposed method can mitigate the influence of cognitive biases.