Do large language models (LLMs) display rational reasoning? LLMs have been
shown to contain human biases due to the data they have been trained on;
whether this is reflected in rational reasoning remains less clear. In this
paper, we answer this question by evaluating seven language models using tasks
from the cognitive psychology literature. We find that, like humans, LLMs
display irrationality in these tasks. However, the way this irrationality is
displayed does not reflect that shown by humans. When incorrect answers are
given by LLMs to these tasks, they are often incorrect in ways that differ from
human-like biases. On top of this, the LLMs reveal an additional layer of
irrationality in the significant inconsistency of the responses. Aside from the
experimental results, this paper seeks to make a methodological contribution by
showing how we can assess and compare different capabilities of these types of
models, in this case with respect to rational reasoning.

大型语言模型在合理推理中显示出人类的偏见和不合理行为，但这种不合理行为与人类不同，且存在额外的回答不一致性。

大型语言模型中的 (非) 理性和认知偏差

(Ir)rationality and Cognitive Biases in Large Language Models

Chain of Thought (CoT) is significant in improving the reasoning abilities of
large language models (LLMs). However, the correlation between the
effectiveness of CoT and the length of reasoning steps in prompts remains
largely unknown. To shed light on this, we have conducted several empirical
experiments to explore the relations. Specifically, we design experiments that
expand and compress the rationale reasoning steps within CoT demonstrations,
while keeping all other factors constant. We have the following key findings.
First, the results indicate that lengthening the reasoning steps in prompts,
even without adding new information into the prompt, considerably enhances
LLMs' reasoning abilities across multiple datasets. Alternatively, shortening
the reasoning steps, even while preserving the key information, significantly
diminishes the reasoning abilities of models. This finding highlights the
importance of the number of steps in CoT prompts and provides practical
guidance to make better use of LLMs' potential in complex problem-solving
scenarios. Second, we also investigated the relationship between the
performance of CoT and the rationales used in demonstrations. Surprisingly, the
result shows that even incorrect rationales can yield favorable outcomes if
they maintain the requisite length of inference. Third, we observed that the
advantages of increasing reasoning steps are task-dependent: simpler tasks
require fewer steps, whereas complex tasks gain significantly from longer
inference sequences.

通过一系列实验，研究发现链式思维（CoT）在大型语言模型（LLMs）的推理能力提升中起到重要作用，推理步骤的长度与 CoT 的有效性之间的相关性还不完全清楚，本研究对此进行了探索，并得出了以下主要发现：1. 增加问题中推理步骤的长度能显著提高 LLMs 在多个数据集上的推理能力；2. 即使使用的推理方法有误，只要保持了必要的推理步骤长度，仍能取得良好的结果；3. 推理步骤的增加对任务的优势取决于任务的复杂性。这些发现对于在复杂问题解决场景中更好地利用 LLMs 的潜力提供了实际指导。