理解用户画像在大型语言模型个性化中的作用
Understanding the Role of User Profile in the Personalization of Large Language Models
AI总结：
本研究探讨了用户画像在LLMs个性化中的作用，发现其有效性源于个性化信息，历史个性化回复起关键作用，且置于输入开头位置的画像影响更大，为有效利用用户画像提供了指导。
摘要：
利用用户画像来个性化大型语言模型（LLMs）已被证明能够提升在广泛任务上的性能。然而，用户画像的精确作用及其对LLMs的影响机制仍不清晰。本研究首先确认，用户画像的有效性主要归因于个性化信息而非语义信息。此外，我们调查了用户画像如何影响LLMs的个性化。在用户画像内部，我们揭示出由用户产生或认可的历史个性化回复在个性化LLMs中起着关键作用。这一发现解锁了LLMs在有限输入长度限制内纳入更多用户画像的潜力。至于用户画像的位置，我们观察到整合到输入上下文不同位置的用户画像对个性化的贡献并不均等。相反，更靠近开头的用户画像对LLMs的个性化影响更大。我们的发现揭示了用户画像对LLMs个性化的作用，并展示了纳入用户画像如何影响性能，为有效利用用户画像提供了见解。
英文摘要：
Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance the performance on a wide range of tasks. However, the precise role of user profiles and their effect mechanism on LLMs remains unclear. This study first confirms that the effectiveness of user profiles is primarily due to personalization information rather than semantic information. Furthermore, we investigate how user profiles affect the personalization of LLMs. Within the user profile, we reveal that it is the historical personalized response produced or approved by users that plays a pivotal role in personalizing LLMs. This discovery unlocks the potential of LLMs to incorporate a greater number of user profiles within the constraints of limited input length. As for the position of user profiles, we observe that user profiles integrated into different positions of the input context do not contribute equally to personalization. Instead, where the user profile that is closer to the beginning affects more on the personalization of LLMs. Our findings reveal the role of user profiles for the personalization of LLMs, and showcase how incorporating user profiles impacts performance providing insight to leverage user profiles effectively.
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。
