用低成本提示技术让人类与大模型协作提升文本分类准确性
Complementary Learning Approach for Text Classification using Large Language Models
- 结合思维链与少量样本提示，实现人机协同推理
- 在1934篇医药联盟新闻稿上验证了人机评分差异的可解释性
- 适合关注人机协作、低资源文本分类的研究者
本研究提出一种结构化方法，以低成本、简洁的方式利用大语言模型（LLMs），融合学者与机器的优势，弥补彼此弱点。通过计算机科学中的思维链与少样本提示，将定性研究中最佳合作实践拓展至定量研究中的人机团队。该方法使人类能够运用归纳推理和自然语言，不仅检验机器的决策，也反思自身的判断。我们展示了如何使用该方法分析1934篇1990-2017年间发布的医药联盟新闻稿中的人机评分差异，揭示了如何通过精心设计的低成本策略管理LLMs的固有缺陷。
原文摘要 · Abstract (English)
In this study, we propose a structured methodology that utilizes large language models (LLMs) in a cost-efficient and parsimonious manner, integrating the strengths of scholars and machines while offsetting their respective weaknesses. Our methodology, facilitated through a chain of thought and few-shot learning prompting from computer science, extends best practices for co-author teams in qualitative research to human-machine teams in quantitative research. This allows humans to utilize abductive reasoning and natural language to interrogate not just what the machine has done but also what the human has done. Our method highlights how scholars can manage inherent weaknesses OF LLMs using careful, low-cost techniques. We demonstrate how to use the methodology to interrogate human-machine rating discrepancies for a sample of 1,934 press releases announcing pharmaceutical alliances (1990-2017).
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