Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909645571883008 |
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| author | Qiu, Yilun Zhao, Xiaoyan Zhang, Yang Bai, Yimeng Wang, Wenjie Cheng, Hong Feng, Fuli Chua, Tat-Seng |
| author_facet | Qiu, Yilun Zhao, Xiaoyan Zhang, Yang Bai, Yimeng Wang, Wenjie Cheng, Hong Feng, Fuli Chua, Tat-Seng |
| contents | Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual's historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code at https://github.com/SnowCharmQ/DPL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization Qiu, Yilun Zhao, Xiaoyan Zhang, Yang Bai, Yimeng Wang, Wenjie Cheng, Hong Feng, Fuli Chua, Tat-Seng Computation and Language Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual's historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code at https://github.com/SnowCharmQ/DPL. |
| title | Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.02450 |