LLMs + Persona-Plug = 个性化LLMs

摘要

个性化在许多语言任务和应用中起着关键作用，因为具有相同需求的用户可能基于其个人兴趣而偏好不同的输出。这导致了各种个性化方法的发展，旨在调整大型语言模型（LLMs）以生成与用户偏好一致的定制输出。其中一些方法涉及为每个用户微调独特的个性化LLM，这对广泛应用来说成本太高。替代方法以即插即用的方式引入个性化信息，通过检索用户的相关历史文本作为示例。然而，这种基于检索的策略可能会打破用户历史的连续性，无法捕捉用户的整体风格和模式，从而导致性能不佳。为了解决这些挑战，我们提出了一种新颖的个性化LLM模型。通过模拟所有用户的历史上下文，它为每个个体构建了一个用户特定的嵌入，通过轻量级即插即用用户嵌入模块。通过将这个嵌入附加到任务输入，LLMs可以更好地理解和捕捉用户习惯和偏好，从而生成更个性化的输出，而无需调整自己的参数。在语言模型个性化（LaMP）基准测试中进行的大量实验表明，所提出的模型明显优于现有的个性化LLM方法。

English

Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user's relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user's overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, . It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their own parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches.

LLMs + Persona-Plug = 个性化LLMs

LLMs + Persona-Plug = Personalized LLMs

摘要

Summary

Support

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