引导式画像生成提升大语言模型的个性化能力
Guided Profile Generation Improves Personalization with LLMs
AI总结：
提出引导式画像生成（GPG）方法，通过生成自然语言个人画像，提升大语言模型在个性化任务中的表现，偏好预测准确率提高37%。
摘要：
在现代商业系统中，包括推荐、排序和电子商务平台，通过将个性化上下文作为输入纳入大语言模型（LLMs）来改善客户体验已成为一种趋势。然而，在没有额外处理或上下文增强的情况下，LLMs往往难以有效解析和利用稀疏且复杂的个人上下文，这凸显了对更复杂上下文理解机制的需求。在这项工作中，我们提出了引导式画像生成（GPG），一种生成自然语言个人画像的通用方法。据观察，中间引导式画像生成使LLMs能够从个人上下文中总结并提取重要、独特的特征，形成简洁、描述性的句子，从而更精确地使生成内容贴合个体的独特习惯和偏好。我们的实验结果表明，GPG在不同任务上提升了LLM的个性化能力，例如，与直接将原始个人上下文输入LLMs相比，在预测个人偏好方面准确率提高了37%。
英文摘要：
In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLMs). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual's unique habits and preferences. Our experimental results show that GPG improves LLM's personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.
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