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

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arxiv 2311.09180 v2 pith:H6DAOAQC submitted 2023-11-15 cs.CL cs.HCcs.IR

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

Sheshera Mysore , Zhuoran Lu , Mengting Wan , Longqi Yang , Bahareh Sarrafzadeh , Steve Menezes , Tina Baghaee , Emmanuel Barajas Gonzalez
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Jennifer Neville Tara Safavi
This is my paper
classification cs.CL cs.HCcs.IR
keywords retrievergenerationgeneration-calibratedpearlpersonalizationqualitywritingassistants
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 8 Pith papers

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