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    <abstract>In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLM). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual’s unique habits and preferences. Our experimental results show that GPG improves LLM’s personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.</abstract>
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%0 Conference Proceedings
%T Guided Profile Generation Improves Personalization with Large Language Models
%A Zhang, Jiarui
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-2024-guided
%X In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLM). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual’s unique habits and preferences. Our experimental results show that GPG improves LLM’s personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.
%U https://aclanthology.org/2024.findings-emnlp.231
%P 4005-4016

Markdown (Informal)

[Guided Profile Generation Improves Personalization with Large Language Models](https://aclanthology.org/2024.findings-emnlp.231) (Zhang, Findings 2024)

• Guided Profile Generation Improves Personalization with Large Language Models (Zhang, Findings 2024)

ACL

• Jiarui Zhang. 2024. Guided Profile Generation Improves Personalization with Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4005–4016, Miami, Florida, USA. Association for Computational Linguistics.