Large language models (LLMs) have significantly advanced Natural Language
Processing (NLP) tasks in recent years. However, their universal nature poses
limitations in scenarios requiring personalized responses, such as
recommendation systems and chatbots. This paper investigates methods to
personalize LLMs, comparing fine-tuning and zero-shot reasoning approaches on
subjective tasks. Results demonstrate that personalized fine-tuning improves
model reasoning compared to non-personalized models. Experiments on datasets
for emotion recognition and hate speech detection show consistent performance
gains with personalized methods across different LLM architectures. These
findings underscore the importance of personalization for enhancing LLM
capabilities in subjective text perception tasks.

该研究探讨了个性化调整和零 - shot 推理方法在主观任务上的应用，结果表明，与非个性化模型相比，个性化调整能够提高模型的推理能力，并在不同的大语言模型架构上实现了对情感识别和仇恨言论检测等数据集的一致性性能提升，这些发现凸显了个性化对于增强大语言模型在主观文本感知任务中的重要性。