---
title: "Democratizing Large Language Models via Personalized"
description: "Most current approaches to personalizing large language models work by putting user information… 55 references, cited by 54 papers here."
authors: ["Zhaoxuan Tan", "Qingkai Zeng", "Yijun Tian", "Zheyuan Liu", "Bing Yin", "Meng Jiang"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.372/"
doi: "10.18653/v1/2024.emnlp-main.372"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tan-2024-democratizing-large-language-models-personalized/"
---
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# Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning

**Authors:** [Zhaoxuan Tan](https://md.research.elamur.ai/free/en/authors/zhaoxuan-tan/), [Qingkai Zeng](https://md.research.elamur.ai/free/en/authors/qingkai-zeng/), [Yijun Tian](https://md.research.elamur.ai/free/en/authors/yijun-tian/), [Zheyuan Liu](https://md.research.elamur.ai/free/en/authors/zheyuan-liu/), [Bing Yin](https://md.research.elamur.ai/free/en/authors/bing-yin/) and [Meng Jiang](https://md.research.elamur.ai/free/en/authors/meng-jiang/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.372/) · [PDF](https://aclanthology.org/2024.emnlp-main.372.pdf) · [DOI 10.18653/v1/2024.emnlp-main.372](https://doi.org/10.18653/v1/2024.emnlp-main.372) · [OpenAlex](https://openalex.org/W4404782947)

**Citations:** 29 citations (OpenAlex, 2026-10-14) · [54](https://md.research.elamur.ai/free/en/papers/tan-2024-democratizing-large-language-models-personalized/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary

Most current approaches to personalizing large language models work by putting user information into the prompt. They add retrieved items from a user's history or a generated textual profile, and a shared central model processes the result. The authors identify two weaknesses. Users do not own the model, which limits customization and exposes their data. And retrieval struggles when a person's new behavior differs from their past activity. Their framework, One PEFT Per User (OPPU), gives each user a small private parameter-efficient module, LoRA by default, trained on that person's history. The module plugs into a frozen, task-adapted base LLM. OPPU stores user knowledge in these parameters and can also combine it with retrieval and profile augmentation. Users who prioritize privacy can skip the augmentation and avoid sending their data to a provider. On all seven tasks of the LaMP benchmark, OPPU outperforms prompt-based baselines. It is especially strong when user behavior shifts. The work points toward modular, user-owned personal LLMs.

## Key points

- Evaluation uses the LaMP benchmark: seven public personalization tasks, four classification and three generation.
- The base LLMs are first task-adapted with LoRA on held-out users, updating roughly 0.5% of parameters. Three variants are built: plain, retrieval-augmented, and profile-augmented, with profiles produced by Vicuna.
- When a user's history does not match the query–answer format, as in tweet paraphrasing, the personal module is trained with next-token prediction on the raw history texts.
- Beyond the main results, OPPU stays robust across history formats and users with different activity levels. It also works with other PEFT methods, such as prompt tuning.
- A stated limitation is that the data covers only one task per user, so cross-domain personalization is left for future work. Code: github.com/TamSiuhin/OPPU.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Notre Dame](https://md.research.elamur.ai/free/en/universities/university-of-notre-dame/) — University, Notre Dame, United States · 24 works · 36,415 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 1,344 works

## Cite

Cite as: Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin and Meng Jiang. 2024. Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tan2024democratizing,
  title = {{Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning}},
  author = {Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin and Meng Jiang},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.372},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.372}
}
```

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## References

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*Updated 24 October 2026*