PEARL: Personalizing Large Language Model Writing Assistants with
  Generation-Calibrated Retrievers
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.
6555ab778094dce8237364d2
Sheshera Mysore
admin_assigned
2023-11-16T10:06:10.703Z
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Sheshera Mysore
user
Sheshera
6555ab778094dce8237364d3
Zhuoran Lu
6555ab778094dce8237364d4
Mengting Wan
claimed_verified
2024-03-27T08:46:39.220Z
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Mengting Wan
user
mengtingwan
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Longqi Yang
admin_assigned
2023-11-16T10:06:39.703Z
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Longqi Yang
user
ylongqi
6555ab778094dce8237364d6
Steve Menezes
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Tina Baghaee
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Emmanuel Barajas Gonzalez
6555ab778094dce8237364d9
Jennifer Neville
6555ab778094dce8237364da
Tara Safavi
extracted_pending
2023-11-16T05:41:12.149Z
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/avatars/0820cad83df012182cdf4094a7dcf744.svg
Tara Safavi
user
tararootcake
PEARL: Personalizing Large Language Model Writing Assistants with
  Generation-Calibrated Retrievers
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, a retrieval-augmented writing assistant, personalizes large language model outputs for users by selecting relevant documents, improving the quality of workplace social media posts and Reddit comments.
large language models writing assistants personalization retriever prompt augmentation training data selection KL-divergence objective generation-calibrated retriever low-quality generations LLM chaining