On the Way to LLM Personalization: Learning to Remember User Conversations

TL;DR

This paper introduces PLUM, a method for personalizing large language models by injecting prior conversation knowledge through data augmentation and parameter-efficient fine-tuning, improving personalized response accuracy.

Contribution

The paper proposes PLUM, a novel pipeline combining data augmentation and low-rank adaptation for effective user conversation personalization in LLMs.

Findings

Achieved 81.5% accuracy on 100 conversations.

Demonstrated competitive performance against RAG baseline.

Addressed real-world constraints of sequential data and per-user personalization.

Abstract

Large Language Models (LLMs) have quickly become an invaluable assistant for a variety of tasks. However, their effectiveness is constrained by their ability to tailor responses to human preferences and behaviors via personalization. Prior work in LLM personalization has largely focused on style transfer or incorporating small factoids about the user, as knowledge injection remains an open challenge. In this paper, we explore injecting knowledge of prior conversations into LLMs to enable future work on less redundant, personalized conversations. We identify two real-world constraints: (1) conversations are sequential in time and must be treated as such during training, and (2) per-user personalization is only viable in parameter-efficient settings. To this aim, we propose PLUM, a pipeline performing data augmentation for up-sampling conversations as question-answer pairs, that are then used to finetune a low-rank adaptation adapter with a weighted cross entropy loss. Even in this first exploration of the problem, we perform competitively with baselines such as RAG, attaining an accuracy of 81.5% across 100 conversations.