个性化大型语言模型
Personalized Large Language Models
AI总结：
本文研究个性化大型语言模型，通过比较微调和零样本推理，证明个性化微调能提升模型在主观任务上的推理与性能。
摘要：
近年来，大型语言模型（LLMs）显著推进了自然语言处理（NLP）任务。然而，其通用性在需要个性化响应的场景（如推荐系统和聊天机器人）中构成局限。本文研究了个性化LLMs的方法，在主观任务上比较了微调与零样本推理方法。结果表明，与未个性化模型相比，个性化微调提升了模型推理能力。在情感识别和仇恨言论检测数据集上的实验显示，不同LLM架构下个性化方法均带来一致的性能提升。这些发现强调了个性化对于增强LLM在主观文本感知任务中能力的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
