Large Language Models (LLMs) like GPT-4 and Gemini have significantly advanced artificial intelligence by enabling machines to generate and comprehend human-like text. Despite their impressive capabilities, LLMs are not immune to limitations, including various biases. While much research has explored demographic biases, the cognitive biases in LLMs have not been equally scrutinized. This study delves into anchoring bias, a cognitive bias where initial information disproportionately influences judgment. Utilizing an experimental dataset, we examine how anchoring bias manifests in LLMs and verify the effectiveness of various mitigation strategies. Our findings highlight the sensitivity of LLM responses to biased hints. At the same time, our experiments show that, to mitigate anchoring bias, one needs to collect hints from comprehensive angles to prevent the LLMs from being anchored to individual pieces of information, while simple algorithms such as Chain-of-Thought, Thoughts of Principles, Ignoring Anchor Hints, and Reflection are not sufficient.

翻译：诸如GPT-4和Gemini等大型语言模型（LLMs）通过使机器能够生成和理解类人文本，显著推动了人工智能的发展。尽管其能力令人印象深刻，LLMs仍无法避免各种局限性，包括多种偏差。虽然已有大量研究探讨了人口统计偏差，但LLMs中的认知偏差尚未得到同等程度的审视。本研究深入探讨了锚定偏差——一种初始信息对判断产生不成比例影响的认知偏差。通过利用实验数据集，我们检验了锚定偏差在LLMs中的表现形式，并验证了多种缓解策略的有效性。我们的研究结果突显了LLM响应对有偏提示的敏感性。同时，实验表明，为缓解锚定偏差，需要从全面角度收集提示以防止LLMs被个别信息锚定，而诸如思维链、原则思考、忽略锚定提示和反思等简单算法并不足以解决问题。