揭示大模型新闻推荐中的认知偏差及其危害
Cognitive Biases in Large Language Models for News Recommendation
- 分析锚定、框架、现状和群体归因等认知偏差在新闻推荐中的影响
- 发现这些偏差会导致假信息传播、刻板印象强化和回音室效应
- 提出数据增强、提示工程和学习算法三种缓解策略
尽管大语言模型（LLMs）在新闻推荐系统中日益重要，但其应用引入了新风险，即模型内存在的认知偏差。认知偏差指判断过程中系统性偏离规范或理性的模式，可能导致大模型输出失真，威胁新闻推荐系统的可靠性。具体而言，受认知偏差影响的基于大模型的新闻推荐系统可能引发假信息传播、刻板印象强化及回音室形成。本文探讨了锚定偏差、框架偏差、现状偏差和群体归因偏差对基于大模型的新闻推荐系统的影响。为进一步提升此类系统可靠性，文章从数据增强、提示工程和学习算法三个方面讨论了缓解策略。
原文摘要 · Abstract (English)
Despite large language models (LLMs) increasingly becoming important components of news recommender systems, employing LLMs in such systems introduces new risks, such as the influence of cognitive biases in LLMs. Cognitive biases refer to systematic patterns of deviation from norms or rationality in the judgment process, which can result in inaccurate outputs from LLMs, thus threatening the reliability of news recommender systems. Specifically, LLM-based news recommender systems affected by cognitive biases could lead to the propagation of misinformation, reinforcement of stereotypes, and the formation of echo chambers. In this paper, we explore the potential impact of multiple cognitive biases on LLM-based news recommender systems, including anchoring bias, framing bias, status quo bias and group attribution bias. Furthermore, to facilitate future research at improving the reliability of LLM-based news recommender systems, we discuss strategies to mitigate these biases through data augmentation, prompt engineering and learning algorithms aspects.
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