使大型语言模型行为与人类引用偏好对齐
Aligning Large Language Model Behavior with Human Citation Preferences
AI总结：
本研究通过构建数据集分析LLM引用行为与人类偏好的一致性，发现模型在引用医学文本和数字句子上存在偏差，通过优化可提升对齐效果。
摘要：
大多数基于强大大规模语言模型（LLMs）的服务会在输出中添加引用以增强可信度。最近的研究逐渐关注到一个问题，即应链接哪些参考文献。然而，LLMs如何识别引用价值以及这一过程应如何控制仍处于探索阶段。本研究聚焦于LLMs目前倾向于引用哪些内容以及这种行为与人类偏好的一致性。我们构建了一个数据集来表征人类引用偏好与LLM行为之间的关系。网络衍生文本被分类为八种引用动机类型，所有类型组合的成对引用偏好被全面评估，以捕捉细致的对比。我们的结果表明，人类最常寻求对医学文本的引用，而更强的模型表现出类似倾向。我们还发现，当前模型比人类更有可能在需要引用的来源如维基百科上添加引用，这种过度引用会降低对齐准确性。相反，模型系统性地低估了数字句子（相对于人类减少22.6%）和包含个人姓名的句子（减少20.1%），这些类别通常是人类要求引用的。此外，通过直接偏好优化实验发现，模型行为可以校准以更好地匹配人类引用偏好。我们预计本研究将为更细致地研究LLM引用偏好提供基础。
英文摘要：
Most services built on powerful large-scale language models (LLMs) add citations to their output to enhance credibility. Recent research has paid increasing attention to the question of what reference documents to link to outputs. However, how LLMs recognize cite-worthiness and how this process should be controlled remains underexplored. In this study, we focus on what kinds of content LLMs currently tend to cite and how well that behavior aligns with human preferences. We construct a dataset to characterize the relationship between human citation preferences and LLM behavior. Web-derived texts are categorized into eight citation-motivation types, and pairwise citation preferences are exhaustively evaluated across all type combinations to capture fine-grained contrasts. Our results show that humans most frequently seek citations for medical text, and stronger models display a similar tendency. We also find that current models are as much as $27\%$ more likely than humans to add citations to text that is explicitly marked as needing citations on sources such as Wikipedia, and this overemphasis reduces alignment accuracy. Conversely, models systematically underselect numeric sentences (by $-22.6\%$ relative to humans) and sentences containing personal names (by $-20.1\%$), categories for which humans typically demand citations. Furthermore, experiments with Direct Preference Optimization demonstrate that model behavior can be calibrated to better match human citation preferences. We expect this study to provide a foundation for more fine-grained investigations into LLM citation preferences.
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