@inproceedings{magister-etal-2025-way,
title = "On the Way to {LLM} Personalization: Learning to Remember User Conversations",
author = "Magister, Lucie Charlotte and
Metcalf, Katherine and
Zhang, Yizhe and
Ter Hoeve, Maartje",
editor = "Jia, Robin and
Wallace, Eric and
Huang, Yangsibo and
Pimentel, Tiago and
Maini, Pratyush and
Dankers, Verna and
Wei, Johnny and
Lesci, Pietro",
booktitle = "Proceedings of the First Workshop on Large Language Model Memorization (L2M2)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.l2m2-1.5/",
doi = "10.18653/v1/2025.l2m2-1.5",
pages = "61--77",
ISBN = "979-8-89176-278-7",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T On the Way to LLM Personalization: Learning to Remember User Conversations
%A Magister, Lucie Charlotte
%A Metcalf, Katherine
%A Zhang, Yizhe
%A Ter Hoeve, Maartje
%Y Jia, Robin
%Y Wallace, Eric
%Y Huang, Yangsibo
%Y Pimentel, Tiago
%Y Maini, Pratyush
%Y Dankers, Verna
%Y Wei, Johnny
%Y Lesci, Pietro
%S Proceedings of the First Workshop on Large Language Model Memorization (L2M2)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-278-7
%F magister-etal-2025-way
%X 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.
%R 10.18653/v1/2025.l2m2-1.5
%U https://aclanthology.org/2025.l2m2-1.5/
%U https://doi.org/10.18653/v1/2025.l2m2-1.5
%P 61-77
Markdown (Informal)
[On the Way to LLM Personalization: Learning to Remember User Conversations](https://aclanthology.org/2025.l2m2-1.5/) (Magister et al., L2M2 2025)
ACL