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

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本文提出了一种使用进化算法生成大型语言模型(LLM)的零样本推理链(CoT)提示的新方法。通过动态创建和选择适合任务的CoT提示,该方法在10个推理数据集上的实验中优于现有技术,特别是在算术和符号推理方面。研究还强调了这种方法的适应性和增强LLM推理能力的潜力。

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本文是LLM系列文章,针对《Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models》的翻译。

大型语言模型中进化算法指导下的零样本思维链推理

摘要

大型语言模型(LLM)通过应用零样本思维链(CoT)提示,在不同的任务中表现出了显著的性能,并表现出了令人印象深刻的推理能力。然而,由于句子前缀在预训练阶段的演变性质,在所有任务实例中使用相同CoT提示的现有零样本CoT提示方法可能不是最佳的。在本文中,我们介绍了一种新颖的零样本提示方法,该方法利用进化算法动态生成LLM的不同提示。我们的方法包括初始化两个CoT提示,基于LLM执行进化操作以创建不同的集合,并利用LLM为给定问题选择合适的CoT提示。此外,在所选CoT提示的指导下,重写操作增强了LLM对该问题的理解。在10个推理数据集上进行的大量实验表明,与GPT-3.5-turbo和GPT-4上当前的零样本CoT提示方法相比,我们提出的方法具有优越的性能。此外,深入的分析实验强调了我们的方法在各种推理任务中的适应性和有效性。

1 引言

2 前言

3 方法

4 实验

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### Chain-of-Thought Prompting Mechanism in Large Language Models In large language models, chain-of-thought prompting serves as a method to enhance reasoning capabilities by guiding the model through structured thought processes. This approach involves breaking down complex problems into simpler components and providing step-by-step guidance that mirrors human cognitive processing. The creation of these prompts typically includes selecting examples from training datasets where each example represents part of an overall problem-solving process[^2]. By decomposing tasks into multiple steps, this technique encourages deeper understanding and more accurate predictions compared to traditional methods. For instance, when faced with multi-hop question answering or logical deduction challenges, using such chains allows models not only to generate correct answers but also articulate intermediate thoughts leading up to those conclusions. Such transparency facilitates better interpretability while improving performance on various NLP benchmarks. ```python def create_chain_of_thought_prompt(task_description, examples): """ Creates a chain-of-thought prompt based on given task description and examples. Args: task_description (str): Description of the task at hand. examples (list): List containing tuples of input-output pairs used for demonstration purposes. Returns: str: Formatted string representing the final prompt including both instructions and sample cases. """ formatted_examples = "\n".join([f"Input: {ex[0]}, Output: {ex[1]}" for ex in examples]) return f""" Task: {task_description} Examples: {formatted_examples} Now try solving similar questions following above pattern. """ # Example usage examples = [ ("What color do you get mixing red and blue?", "Purple"), ("If it rains tomorrow, will we have our picnic?", "No") ] print(create_chain_of_thought_prompt("Solve logic puzzles", examples)) ```
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