大模型更偏爱正式、无错别字的文本，学习效率更高。
Formality is Favored: Unraveling the Learning Preferences of Large Language Models on Data with Conflicting Knowledge
- 通过分析冲突知识数据，发现模型偏好正式且错误少的文本。
- 大型模型对这类文本的学习速度更快，处理冲突时更信任其内容。
- 该偏好可被人为调控，适用于模型优化与训练数据筛选。
经过海量预训练数据训练的大语言模型在众多知识密集型任务中表现优异。然而，预训练数据常包含误导性甚至相互矛盾的信息，值得探究模型在训练过程中如何处理此类噪声数据。本研究系统分析了大语言模型对存在冲突知识数据的学习偏好。结果表明，预训练模型的学习偏好与人类相似，即更倾向于正式文本和拼写错误较少的文本，面对冲突时能更快学习并更优地处理这些特征的数据。这一现象在不同模型和语言间具有普适性，且在更大模型中更为显著。深入分析显示，模型倾向于信任与多数数据一致性的特征，通过操控数据与多数数据的一致性程度，可实现新偏好的建立或旧偏好的消除。
原文摘要 · Abstract (English)
Having been trained on massive pretraining data, large language models have shown excellent performance on many knowledge-intensive tasks. However, pretraining data tends to contain misleading and even conflicting information, and it is intriguing to understand how LLMs handle these noisy data during training. In this study, we systematically analyze LLMs' learning preferences for data with conflicting knowledge. We find that pretrained LLMs establish learning preferences similar to humans, i.e., preferences towards formal texts and texts with fewer spelling errors, resulting in faster learning and more favorable treatment of knowledge in data with such features when facing conflicts. This finding is generalizable across models and languages and is more evident in larger models. An in-depth analysis reveals that LLMs tend to trust data with features that signify consistency with the majority of data, and it is possible to instill new preferences and erase old ones by manipulating the degree of consistency with the majority data.
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