ComMer: a Framework for Compressing and Merging User Data for Personalization
Journal:
arXiv
Published Date:
Jan 5, 2025
Abstract
Large Language Models (LLMs) excel at a wide range of tasks, but adapting
them to new data, particularly for personalized applications, poses significant
challenges due to resource and computational constraints. Existing methods
either rely on exposing fresh data to the model through the prompt, which is
limited by context size and computationally expensive at inference time, or
fine-tuning, which incurs substantial training and update costs. In this paper,
we introduce ComMer - Compress and Merge - a novel framework that efficiently
personalizes LLMs by compressing users' documents into compact representations,
which are then merged and fed into a frozen LLM. We evaluate ComMer on two
types of personalization tasks - personalized skill learning, using the tweet
paraphrasing dataset and the personalized news headline generation dataset from
the LaMP benchmark, and knowledge-intensive, using the PerLTQA dataset. Our
experiments demonstrate that in constrained inference budget scenarios ComMer
achieves superior quality in skill learning tasks, while highlighting
limitations in knowledge-intensive settings due to the loss of detailed
information. These results offer insights into trade-offs and potential
optimizations in multi-document compression for personalization.
