Pearl：利用生成校准检索器个性化大型语言模型写作助手
Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
AI总结：
针对LLM写作助手缺乏个性化的问题，提出Pearl，利用生成校准检索器选择历史文档增强提示，通过数据选择与尺度校准KL散度训练，在社交媒体数据集上提升长文生成质量，并可预测性能。
摘要：
强大的大型语言模型促进了写作助手的发展，这些助手有望显著提高写作与沟通的质量和效率。然而，有效辅助的一个障碍是LLM输出缺乏对作者沟通风格、专业知识和价值观的个性化。在本文中，我们通过提出Pearl来解决这一挑战，这是一种配备检索器的LLM写作助手，该检索器经过训练以实现个性化的生成校准。生成校准确保我们的检索器选择历史用户撰写的文档来增强LLM提示，从而可能帮助LLM生成更好地符合用户的偏好。我们提出了训练此类检索器的两个关键新颖点：（1）一种训练数据选择方法，用于识别可能从个性化中受益的用户请求以及能提供这种益处的文档；（2）一种尺度校准的KL散度目标，确保我们的检索器得分与使用该文档进行个性化生成的后续生成质量成正比。在一系列整体评估中，我们证明了Pearl在多个社交媒体数据集上生成长篇文本的有效性。最后，我们展示了生成校准检索器如何兼作性能预测器——检测低质量检索，并通过LLM修订来改进可能表现不佳的输出。
英文摘要：
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, specialized knowledge, and values. In this paper, we address this challenge by proposing Pearl, a LLM writing assistant personalized with a retriever that is trained to be generation-calibrated for personalization. Generation calibration ensures that our retriever selects historic user authored documents to augment an LLM prompt such that they are likely to help an LLM generation better adhere to a users' preferences. We propose two key novelties for training such a 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 scores remain proportional to the downstream generation quality from using the document for personalized generation. In a series of holistic evaluations, we demonstrate the effectiveness of Pearl in generating long-form texts on multiple social media datasets. Finally, we demonstrate how a generation-calibrated retriever can double as a performance predictor -- detecting low quality retrieval, and improving potentially under-performing outputs via revision with LLMs.
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。
