Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication. However, a barrier to effective assistance is the lack of personalization in LLM outputs to the author's communication style and specialized knowledge. In this paper, we address this challenge by proposing PEARL, a retrieval-augmented LLM writing assistant personalized with a generation-calibrated retriever. Our retriever is trained to select historic user-authored documents for prompt augmentation, such that they are likely to best personalize LLM generations for a user request. We propose two key novelties for training our retriever: 1) A training data selection method that identifies user requests likely to benefit from personalization and documents that provide that benefit; and 2) A scale-calibrating KL-divergence objective that ensures that our retriever closely tracks the benefit of a document for personalized generation. We demonstrate the effectiveness of PEARL in generating personalized workplace social media posts and Reddit comments. Finally, we showcase the potential of a generation-calibrated retriever to double as a performance predictor and further improve low-quality generations via LLM chaining.

翻译：强大的大语言模型推动了写作助手的发展，有望显著提升写作与沟通的质量与效率。然而，有效辅助的障碍在于大语言模型输出缺乏对作者沟通风格与专业知识的个性化适配。本文通过提出PEARL——一种由生成校准检索器增强的个性化大语言模型写作助手——来解决这一挑战。我们的检索器经过训练，可选取用户历史创作文档进行提示增强，使其能够针对用户请求最佳地个性化大语言模型的生成结果。我们提出两项训练检索器的关键创新：1）一种训练数据选择方法，识别可能从个性化中受益的用户请求及能提供该收益的文档；2）一种尺度校准KL散度目标函数，确保检索器紧密追踪文档对个性化生成的收益。我们通过生成个性化职场社交媒体帖子和Reddit评论，展示了PEARL的有效性。最后，我们揭示了生成校准检索器作为性能预测器以及通过大语言模型链进一步优化低质量生成的潜力。