PEARL：使用生成校準檢索器個性化大型語言模型寫作助手

摘要

強大的大型語言模型已促進了寫作助手的發展，承諾顯著提高組成和溝通的質量和效率。然而，有效協助的一個障礙是大型語言模型輸出中缺乏對作者溝通風格和專業知識的個性化。本文通過提出 PEARL 來應對這一挑戰，這是一個使用生成校準檢索器個性化的擴充型大型語言模型寫作助手。我們的檢索器經過訓練，選擇歷史用戶撰寫的文件進行提示增強，從而最有可能為用戶請求最佳個性化大型語言模型生成。我們提出了兩個訓練檢索器的關鍵創新：1）識別可能受益於個性化的用戶請求和提供該益處的文件的訓練數據選擇方法；2）一個尺度校準的 KL 散度目標，確保我們的檢索器緊密跟踪文件對個性化生成的益處。我們展示了 PEARL 在生成個性化的工作場所社交媒體帖子和 Reddit 評論方面的有效性。最後，我們展示了生成校準檢索器作為性能預測器的潛力，並通過大型語言模型鏈接進一步改進低質量生成。

English

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.

PEARL：使用生成校準檢索器個性化大型語言模型寫作助手

PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers

摘要

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