Large Language Models (LLMs) have demonstrated remarkable performance across
diverse tasks and exhibited impressive reasoning abilities by applying
zero-shot Chain-of-Thought (CoT) prompting. However, due to the evolving nature
of sentence prefixes during the pre-training phase, existing zero-shot CoT
prompting methods that employ identical CoT prompting across all task instances
may not be optimal. In this paper, we introduce a novel zero-shot prompting
method that leverages evolutionary algorithms to generate diverse promptings
for LLMs dynamically. Our approach involves initializing two CoT promptings,
performing evolutionary operations based on LLMs to create a varied set, and
utilizing the LLMs to select a suitable CoT prompting for a given problem.
Additionally, a rewriting operation, guided by the selected CoT prompting,
enhances the understanding of the LLMs about the problem. Extensive experiments
conducted across ten reasoning datasets demonstrate the superior performance of
our proposed method compared to current zero-shot CoT prompting methods on
GPT-3.5-turbo and GPT-4. Moreover, in-depth analytical experiments underscore
the adaptability and effectiveness of our method in various reasoning tasks.

大语言模型通过应用无需预先训练的思维链式促进方法展示了卓越的表现，并展示了出色的推理能力。本文介绍了一种新的零样本促进方法，利用进化算法为大语言模型动态生成多样的促进方式，通过选择适合的促进方式提高模型的理解能力。详细实验结果表明，相较于当前的零样本推理方法，在 GPT-3.5-turbo 和 GPT-4 上，我们的方法表现出更优越的性能。此外，深入的分析实验突显了本方法在各种推理任务中的适应性和有效性。

大型语言模型中演化算法引导的零射链条思维推理

Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms  in Large Language Models

In recent years, personality has been regarded as a valuable personal factor
being incorporated into numerous tasks such as sentiment analysis and product
recommendation. This has led to widespread attention to text-based personality
recognition task, which aims to identify an individual's personality based on
given text. Considering that ChatGPT has recently exhibited remarkable
abilities on various natural language processing tasks, we provide a
preliminary evaluation of ChatGPT on text-based personality recognition task
for generating effective personality data. Concretely, we employ a variety of
prompting strategies to explore ChatGPT's ability in recognizing personality
from given text, especially the level-oriented prompting strategy we designed
for guiding ChatGPT in analyzing given text at a specified level. We compare
the performance of ChatGPT on two representative real-world datasets with
traditional neural network, fine-tuned RoBERTa, and corresponding
state-of-the-art task-specific model. The experimental results show that
ChatGPT with zero-shot chain-of-thought prompting exhibits impressive
personality recognition ability. Triggered by zero-shot chain-of-thought
prompting, ChatGPT outperforms fine-tuned RoBERTa on the two datasets and is
capable to provide natural language explanations through text-based logical
reasoning. Furthermore, relative to zero-shot chain-of-thought prompting,
zero-shot level-oriented chain-of-thought prompting enhances the personality
prediction ability of ChatGPT and reduces the performance gap between ChatGPT
and corresponding state-of-the-art task-specific model. Besides, we also
conduct experiments to observe the fairness of ChatGPT when identifying
personality and discover that ChatGPT shows unfairness to some sensitive
demographic attributes such as gender and age.

ChatGPT 使用零样本的思维链式引导在文本上展示了令人印象深刻的人格识别能力，而在识别人格时，还展示了对某些敏感人口属性（如性别和年龄）的不公平性。