User Profile with Large Language Models: Construction, Updating, and Benchmarking

Fuente: arXiv

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Bibliographic Details
Main Authors:	Prottasha, Nusrat Jahan, Kowsher, Md, Raman, Hafijur, Anny, Israt Jahan, Bhat, Prakash, Garibay, Ivan, Garibay, Ozlem
Format:	Preprint
Published:	2025
Subjects:	Computation and Language
Online Access:	Acceder al recurso
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author	Prottasha, Nusrat Jahan Kowsher, Md Raman, Hafijur Anny, Israt Jahan Bhat, Prakash Garibay, Ivan Garibay, Ozlem
author_facet	Prottasha, Nusrat Jahan Kowsher, Md Raman, Hafijur Anny, Israt Jahan Bhat, Prakash Garibay, Ivan Garibay, Ozlem
contents	User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high-quality open-source user profile datasets: one for profile construction and another for profile updating. These datasets offer a strong basis for evaluating user profile modeling techniques in dynamic settings. We also show a methodology that uses large language models (LLMs) to tackle both profile construction and updating. Our method uses a probabilistic framework to predict user profiles from input text, allowing for precise and context-aware profile generation. Our experiments demonstrate that models like Mistral-7b and Llama2-7b perform strongly in both tasks. LLMs improve the precision and recall of the generated profiles, and high evaluation scores confirm the effectiveness of our approach.
format	Preprint
id	arxiv_https___arxiv_org_abs_2502_10660
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	User Profile with Large Language Models: Construction, Updating, and Benchmarking Prottasha, Nusrat Jahan Kowsher, Md Raman, Hafijur Anny, Israt Jahan Bhat, Prakash Garibay, Ivan Garibay, Ozlem Computation and Language User profile modeling plays a key role in personalized systems, as it requires building accurate profiles and updating them with new information. In this paper, we present two high-quality open-source user profile datasets: one for profile construction and another for profile updating. These datasets offer a strong basis for evaluating user profile modeling techniques in dynamic settings. We also show a methodology that uses large language models (LLMs) to tackle both profile construction and updating. Our method uses a probabilistic framework to predict user profiles from input text, allowing for precise and context-aware profile generation. Our experiments demonstrate that models like Mistral-7b and Llama2-7b perform strongly in both tasks. LLMs improve the precision and recall of the generated profiles, and high evaluation scores confirm the effectiveness of our approach.
title	User Profile with Large Language Models: Construction, Updating, and Benchmarking
topic	Computation and Language
url	https://arxiv.org/abs/2502.10660