用提示工程实时识别文本中的认知偏差，提升内容客观性。
Cognitive Bias Detection Using Advanced Prompt Engineering
- 设计定制化提示词，利用大模型识别常见认知偏差。
- 实验显示对确认偏误等偏差检测准确率高。
- 适合新闻、报告等需客观性的内容创作场景。
认知偏差是判断中系统性偏离理性的现象，严重影响内容的客观性。本文提出一种基于大语言模型（LLMs）和先进提示工程的实时认知偏差检测方法，可识别用户生成文本中的确认偏误、循环论证、隐含假设等常见偏差。通过设计针对性提示词，有效激发大模型的识别与缓解能力，显著提升人类生成内容（如新闻、媒体、报告）的质量。实验结果表明，该方法在识别认知偏差方面具有高准确性，为增强内容客观性、降低偏差决策风险提供了有力工具。
原文摘要 · Abstract (English)
Cognitive biases, systematic deviations from rationality in judgment, pose significant challenges in generating objective content. This paper introduces a novel approach for real-time cognitive bias detection in user-generated text using large language models (LLMs) and advanced prompt engineering techniques. The proposed system analyzes textual data to identify common cognitive biases such as confirmation bias, circular reasoning, and hidden assumption. By designing tailored prompts, the system effectively leverages LLMs' capabilities to both recognize and mitigate these biases, improving the quality of human-generated content (e.g., news, media, reports). Experimental results demonstrate the high accuracy of our approach in identifying cognitive biases, offering a valuable tool for enhancing content objectivity and reducing the risks of biased decision-making.
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