Active Prompting with Chain-of-Thought for Large Language Models

S Diao, P Wang, Y Lin, T Zhang
[The Hong Kong University of Science and Technology & University of Toronto]

大型语言模型基于思维链的主动提示

要点:

  1. 主动提示(Active-Prompt)通过特定任务的示例提示使大型语言模型自适应不同任务；
  2. 采用基于不确定性的主动选择策略，从特定任务查询池中选择最有用的问题进行标注；
  3. 介绍了四种不同的不确定性估计策略：分歧、熵、方差和自信度；
  4. 主动提示在八个广泛使用的算术推理、常识推理和符号推理数据集上取得了可喜的表现，并以很大的优势超过了有竞争力的基线模型。

一句话总结:
提出主动提示(Active-Prompt)，一种让大型语言模型(LLM)适应不同任务的新方法，基于特定任务示例提示，在八个复杂的推理任务上达到了最先进的水平。

The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs' ability to produce high-quality answers. In particular, an effective approach for complex question-and-answer tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving state-of-the-art on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationship demonstrate the effectiveness of our method. Our code will be available at this https URL.

https://arxiv.org/abs/2302.12246
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