---
title: "Instant Personalized Large Language Model Adaptation via"
description: "The authors propose Profile-to-PEFT (P2P), which replaces per-user training with a hypernetwork. 63 references, cited by 1 paper here."
authors: ["Zhaoxuan Tan", "Zixuan Zhang", "Haoyang Wen", "Zheng Li", "Rongzhi Zhang", "Pei Chen", "Fengran Mo", "Zheyuan Liu", "Qingkai Zeng", "Qingyu Yin", "Meng Jiang"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1081/"
doi: "10.18653/v1/2026.acl-long.1081"
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-2026-instant-personalized-large-language-model/"
---
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# Instant Personalized Large Language Model Adaptation via Hypernetwork

**Authors:** [Zhaoxuan Tan](https://md.research.elamur.ai/free/en/authors/zhaoxuan-tan/), [Zixuan Zhang](https://md.research.elamur.ai/free/en/authors/zixuan-zhang/), [Haoyang Wen](https://md.research.elamur.ai/free/en/authors/haoyang-wen/), [Zheng Li](https://md.research.elamur.ai/free/en/authors/zheng-li/), Rongzhi Zhang, [Pei Chen](https://md.research.elamur.ai/free/en/authors/pei-chen/), [Fengran Mo](https://md.research.elamur.ai/free/en/authors/fengran-mo/), [Zheyuan Liu](https://md.research.elamur.ai/free/en/authors/zheyuan-liu/), [Qingkai Zeng](https://md.research.elamur.ai/free/en/authors/qingkai-zeng/), [Qingyu Yin](https://md.research.elamur.ai/free/en/authors/qingyu-yin/) and [Meng Jiang](https://md.research.elamur.ai/free/en/authors/meng-jiang/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **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/2026.acl-long.1081/) · [PDF](https://aclanthology.org/2026.acl-long.1081.pdf) · [DOI 10.18653/v1/2026.acl-long.1081](https://doi.org/10.18653/v1/2026.acl-long.1081) · [OpenAlex](https://openalex.org/W4415951576)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/tan-2026-instant-personalized-large-language-model/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Tailoring LLMs to individual users with parameter-efficient fine-tuning works well. However, the "One-PEFT-Per-User" (OPPU) approach trains a separate adapter for every person, which is too costly for millions of users or for preferences that change over time. The authors propose Profile-to-PEFT (P2P), which replaces per-user training with a hypernetwork. Each user's profile is built from two parts: a base-LLM summary of their history and the past interactions most relevant to the current input. This text is compressed by a frozen sentence encoder into an embedding. An MLP then combines that embedding with learnable module and depth embeddings and outputs LoRA weights for every targeted layer in a single forward pass. The hypernetwork is trained end-to-end with a supervised fine-tuning loss across many users. Experiments cover LaMP, LongLaMP, Personal Reddit and Empathic Conversations. P2P beats prompt-based personalization and OPPU and handles users unseen in training, while running about 33x faster at deployment than OPPU. This makes adapter-based personalization practical at scale. Because adapters can be generated on the user's device, raw data need not be sent to a server.

## Key points
- Baselines include a non-personalized base model, RAG, PAG, full-history prompting, multi-task LoRA and OPPU; OPPU is treated as an oracle because it trains directly on test users' histories. All retrieval uses BM25.
- Generating adapters with P2P is roughly 33x faster at deployment than training them per user with OPPU. Results are supported by LLM-as-a-Judge evaluation and hold for out-of-distribution users.
- Analyses show that the diversity of training users matters more than their number. Performance stays stable across embedding backbones and across users with different activity levels.
- P2P is positioned as the first application of hypernetworks to user-level personalization, as opposed to task-level adaptation (cf. HyperFormer, HyperLoRA, Text-to-LoRA, DnD).
- Stated limitations:
  - Each user has data for only one task.
  - Only LoRA was tested, although Adapter, (IA)3 and prefix tuning should also be compatible.
  - Generated adapters could leak private information.
  - Training the hypernetwork itself remains expensive.

*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

- [Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/) — University, Montreal, Canada · 68 works · 79,382 citations
- [Amazon](https://md.research.elamur.ai/free/en/companies/amazon/) — Company, Seattle, United States · 112 works · 40,565 citations
- [University of Notre Dame](https://md.research.elamur.ai/free/en/universities/university-of-notre-dame/) — University, Notre Dame, United States · 51 works · 36,717 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin and Meng Jiang. 2026. Instant Personalized Large Language Model Adaptation via Hypernetwork. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{tan2026instant,
  title = {{Instant Personalized Large Language Model Adaptation via Hypernetwork}},
  author = {Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin and Meng Jiang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1081},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1081}
}
```

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## References

63 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Avinandan Bose, Zhihan Xiong, Yuejie Chi, Simon Shaolei Du, Lin Xiao, and Maryam Fazel. 2025. Lore: Personalizing llms via low-rank reward modeling. arXiv preprint arXiv:2504.14439. — [arXiv:2504.14439](https://arxiv.org/abs/2504.14439) [find](https://scholar.google.com/scholar?q=Avinandan+Bose%2C+Zhihan+Xiong%2C+Yuejie+Chi%2C+Simon+Shaolei+Du%2C+Lin+Xiao%2C+and+Maryam+Fazel%2E+2025%2E+Lore%3A+Personalizing+llms+via+low%2Drank+reward+modeling%2E+arXiv+preprint+arXiv%3A2504%2E14439%2E+%E2%80%94+%5BarXiv%3A2504%2E1443)
2. Dorian Brown. 2020. Rank-BM25: A Collection of BM25 Algorithms in Python. [find](https://scholar.google.com/scholar?q=Dorian+Brown%2E+2020%2E+Rank%2DBM25%3A+A+Collection+of+BM25+Algorithms+in+Python%2E)
3. Rujikorn Charakorn, Edoardo Cetin, Yujin Tang, and Robert Tjarko Lange. 2025. Text-to-lora: Instant transformer adaption. CoRR, abs/2506.06105. — [arXiv:2506.06105](https://arxiv.org/abs/2506.06105) [find](https://scholar.google.com/scholar?q=Rujikorn+Charakorn%2C+Edoardo+Cetin%2C+Yujin+Tang%2C+and+Robert+Tjarko+Lange%2E+2025%2E+Text%2Dto%2Dlora%3A+Instant+transformer+adaption%2E+CoRR%2C+abs%2F2506%2E06105%2E+%E2%80%94+%5BarXiv%3A2506%2E06105%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2506%2E06105%29)
4. Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, Kai Zheng, Defu Lian, and Enhong Chen. 2024. When large language models meet personalization: perspectives of challenges and opportunities. World Wide Web (WWW), 27(4):42. [find](https://scholar.google.com/scholar?q=Jin+Chen%2C+Zheng+Liu%2C+Xu+Huang%2C+Chenwang+Wu%2C+Qi+Liu%2C+Gangwei+Jiang%2C+Yuanhao+Pu%2C+Yuxuan+Lei%2C+Xiaolong+Chen%2C+Xingmei+Wang%2C+Kai+Zheng%2C+Defu+Lian%2C+and+Enhong+Chen%2E+2024%2E+When+large+language+models+meet+per)
5. Pengyu Cheng, Jiawen Xie, Ke Bai, Yong Dai, and Nan Du. 2023. Everyone deserves a reward: Learning customized human preferences. Preprint, arXiv:2309.03126. — [arXiv:2309.03126](https://arxiv.org/abs/2309.03126) [find](https://scholar.google.com/scholar?q=Pengyu+Cheng%2C+Jiawen+Xie%2C+Ke+Bai%2C+Yong+Dai%2C+and+Nan+Du%2E+2023%2E+Everyone+deserves+a+reward%3A+Learning+customized+human+preferences%2E+Preprint%2C+arXiv%3A2309%2E03126%2E+%E2%80%94+%5BarXiv%3A2309%2E03126%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F)
6. Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023. Uncovering chatgpt’s capabilities in recommender systems. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pag… [find](https://scholar.google.com/scholar?q=Sunhao+Dai%2C+Ninglu+Shao%2C+Haiyuan+Zhao%2C+Weijie+Yu%2C+Zihua+Si%2C+Chen+Xu%2C+Zhongxiang+Sun%2C+Xiao+Zhang%2C+and+Jun+Xu%2E+2023%2E+Uncovering+chatgpt%E2%80%99s+capabilities+in+recommender+systems%2E+In+Proceedings+of+the+17th+)
7. Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for nlp. In International Conference on Machine Learning, pages 2790–2799. PMLR. [find](https://scholar.google.com/scholar?q=Gesmundo%2C+Mona+Attariyan%2C+and+Sylvain+Gelly%2E+2019%2E+Parameter%2Defficient+transfer+learning+for+nlp%2E+In+International+Conference+on+Machine+Learning%2C+pages+2790%E2%80%932799%2E+PMLR%2E)
8. Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, and 1 others. 2021. [Lora: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In International Conference on Learning Representations.
9. Hamish Ivison, Akshita Bhagia, Yizhong Wang, Hannaneh Hajishirzi, and Matthew Peters. 2023. HINT: Hypernetwork instruction tuning for efficient zero-and few-shot generalisation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages… [find](https://scholar.google.com/scholar?q=Hamish+Ivison%2C+Akshita+Bhagia%2C+Yizhong+Wang%2C+Hannaneh+Hajishirzi%2C+and+Matthew+Peters%2E+2023%2E+HINT%3A+Hypernetwork+instruction+tuning+for+efficient+zero%2Dand+few%2Dshot+generalisation%2E+In+Proceedings+of+the+)
10. Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, and Prithviraj Ammanabrolu. 2023. Personalized soups: Personalized large language model alignment via post-hoc parameter merging. arXiv preprint arXiv:2310.11564. [find](https://scholar.google.com/scholar?q=Joel+Jang%2C+Seungone+Kim%2C+Bill+Yuchen+Lin%2C+Yizhong+Wang%2C+Jack+Hessel%2C+Luke+Zettlemoyer%2C+Hannaneh+Hajishirzi%2C+Yejin+Choi%2C+and+Prithviraj+Ammanabrolu%2E+2023%2E+Personalized+soups%3A+Personalized+large+languag)
11. Bowen Jiang, Zhuoqun Hao, Young-Min Cho, Bryan Li, Yuan Yuan, Sihao Chen, Lyle H. Ungar, Camillo J. Taylor, and Dan Roth. 2025. Know me, respond to me: Benchmarking llms for dynamic user profiling and personalized responses at scale. CoRR, abs/2504.14225. — [arXiv:2504.14225](https://arxiv.org/abs/2504.14225) [find](https://scholar.google.com/scholar?q=Bowen+Jiang%2C+Zhuoqun+Hao%2C+Young%2DMin+Cho%2C+Bryan+Li%2C+Yuan+Yuan%2C+Sihao+Chen%2C+Lyle+H%2E+Ungar%2C+Camillo+J%2E+Taylor%2C+and+Dan+Roth%2E+2025%2E+Know+me%2C+respond+to+me%3A+Benchmarking+llms+for+dynamic+user+profiling+and)
12. Yijiang River Dong, Tiancheng Hu, and Nigel Collier. 2024. Can LLM be a personalized judge? In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10126–10141, Miami, Florida, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Yijiang+River+Dong%2C+Tiancheng+Hu%2C+and+Nigel+Collier%2E+2024%2E+Can+LLM+be+a+personalized+judge%3F+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2C+pages+10126%E2%80%9310141%2C+Miami%2C+Florida%2C)
13. Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023. Do llms understand user preferences? evaluating llms on user rating prediction. Preprint, arXiv:2305.06474. — [arXiv:2305.06474](https://arxiv.org/abs/2305.06474) [find](https://scholar.google.com/scholar?q=Wang%2DCheng+Kang%2C+Jianmo+Ni%2C+Nikhil+Mehta%2C+Maheswaran+Sathiamoorthy%2C+Lichan+Hong%2C+Ed+Chi%2C+and+Derek+Zhiyuan+Cheng%2E+2023%2E+Do+llms+understand+user+preferences%3F+evaluating+llms+on+user+rating+prediction%2E+)
14. Jian Guan, Junfei Wu, Jia-Nan Li, Chuanqi Cheng, and Wei Wu. 2025. A survey on personalized Alignment—The missing piece for large language models in real-world applications. In Findings of the Association for Computational Linguistics: ACL 2025, pages 5313–5333, Vienna, Austria. Association for Comp… [find](https://scholar.google.com/scholar?q=Jian+Guan%2C+Junfei+Wu%2C+Jia%2DNan+Li%2C+Chuanqi+Cheng%2C+and+Wei+Wu%2E+2025%2E+A+survey+on+personalized+Alignment%E2%80%94The+missing+piece+for+large+language+models+in+real%2Dworld+applications%2E+In+Findings+of+the+Associa)
15. Jaehyung Kim and Yiming Yang. 2025. Few-shot personalization of LLMs with mis-aligned responses. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 11943–11974, Albu… [find](https://scholar.google.com/scholar?q=Jaehyung+Kim+and+Yiming+Yang%2E+2025%2E+Few%2Dshot+personalization+of+LLMs+with+mis%2Daligned+responses%2E+In+Proceedings+of+the+2025+Conference+of+the+Nations+of+the+Americas+Chapter+of+the+Association+for+Com)
16. David Ha, Andrew M. Dai, and Quoc V. Le. 2016. Hypernetworks. ArXiv, abs/1609.09106. [find](https://scholar.google.com/scholar?q=David+Ha%2C+Andrew+M%2E+Dai%2C+and+Quoc+V%2E+Le%2E+2016%2E+Hypernetworks%2E+ArXiv%2C+abs%2F1609%2E09106%2E)
17. Charles R Harris, K Jarrod Millman, Stéfan J Van Der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J Smith, and 1 others. 2020. [Array programming with numpy](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/). Nature, 585(7825):357–362.
18. Yun He, Steven Zheng, Yi Tay, Jai Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, and 1 others. 2022. Hyperprompt: Prompt-based task-conditioning of transformers. In International conference on machine learning, pages 8678–8690. PMLR. [find](https://scholar.google.com/scholar?q=Yun+He%2C+Steven+Zheng%2C+Yi+Tay%2C+Jai+Gupta%2C+Yu+Du%2C+Vamsi+Aribandi%2C+Zhe+Zhao%2C+YaGuang+Li%2C+Zhao+Chen%2C+Donald+Metzler%2C+and+1+others%2E+2022%2E+Hyperprompt%3A+Prompt%2Dbased+task%2Dconditioning+of+transformers%2E+In+Int)
19. Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Seungone Kim, Jamin Shin, Yejin Choi, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, and Minjoon Seo. 2024. [Prometheus: Inducing fine-grained evaluation…](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/)
20. Hannah Rose Kirk, Bertie Vidgen, Paul Röttger, and Scott A Hale. 2024. The benefits, risks and bounds of personalizing the alignment of large language models to individuals. Nature Machine Intelligence, pages 1–10. [find](https://scholar.google.com/scholar?q=Hannah+Rose+Kirk%2C+Bertie+Vidgen%2C+Paul+R%C3%B6ttger%2C+and+Scott+A+Hale%2E+2024%2E+The+benefits%2C+risks+and+bounds+of+personalizing+the+alignment+of+large+language+models+to+individuals%2E+Nature+Machine+Intelligenc)
21. Ishita Kumar, Snigdha Viswanathan, Sushrita Yerra, Alireza Salemi, Ryan A Rossi, Franck Dernoncourt, 23566 10 Hanieh Deilamsalehy, Xiang Chen, Ruiyi Zhang, Shubham Agarwal, and 1 others. 2024. Longlamp: A benchmark for personalized long-form text generation. arXiv preprint arXiv:2407.11016. — [arXiv:2407.11016](https://arxiv.org/abs/2407.11016) [find](https://scholar.google.com/scholar?q=Ishita+Kumar%2C+Snigdha+Viswanathan%2C+Sushrita+Yerra%2C+Alireza+Salemi%2C+Ryan+A+Rossi%2C+Franck+Dernoncourt%2C+23566+10+Hanieh+Deilamsalehy%2C+Xiang+Chen%2C+Ruiyi+Zhang%2C+Shubham+Agarwal%2C+and+1+others%2E+2024%2E+Longlam)
22. Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, and James Henderson. 2021. [Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/). arXiv preprint arXiv:2106.04489.
23. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023. [Efficient memory management for large language model serving with pagedattention](https://md.research.elamur.ai/free/en/papers/kwon-2023-efficient-memory-management-large-language/). In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles.
24. Sheshera Mysore, Zhuoran Lu, Mengting Wan, Longqi Yang, Steve Menezes, Tina Baghaee, Emmanuel Barajas Gonzalez, Jennifer Neville, and Tara Safavi. 2023. Pearl: Personalizing large language model writing assistants with generation-calibrated retrievers. arXiv preprint arXiv:2311.09180. [find](https://scholar.google.com/scholar?q=Sheshera+Mysore%2C+Zhuoran+Lu%2C+Mengting+Wan%2C+Longqi+Yang%2C+Steve+Menezes%2C+Tina+Baghaee%2C+Emmanuel+Barajas+Gonzalez%2C+Jennifer+Neville%2C+and+Tara+Safavi%2E+2023%2E+Pearl%3A+Personalizing+large+language+model+writi)
25. Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, and Michael Bendersky. 2023. Teach llms to personalize– an approach inspired by writing education. arXiv preprint arXiv:2308.07968. — [arXiv:2308.07968](https://arxiv.org/abs/2308.07968) [find](https://scholar.google.com/scholar?q=Cheng+Li%2C+Mingyang+Zhang%2C+Qiaozhu+Mei%2C+Yaqing+Wang%2C+Spurthi+Amba+Hombaiah%2C+Yi+Liang%2C+and+Michael+Bendersky%2E+2023%2E+Teach+llms+to+personalize%E2%80%93+an+approach+inspired+by+writing+education%2E+arXiv+preprint+a)
26. Lin Ning, Luyang Liu, Jiaxing Wu, Neo Wu, Devora Berlowitz, Sushant Prakash, Bradley Green, Shawn O’Banion, and Jun Xie. 2025. User-llm: Efficient llm contextualization with user embeddings. In Companion Proceedings of the ACM on Web Conference 2025, pages 1219–1223. [find](https://scholar.google.com/scholar?q=Lin+Ning%2C+Luyang+Liu%2C+Jiaxing+Wu%2C+Neo+Wu%2C+Devora+Berlowitz%2C+Sushant+Prakash%2C+Bradley+Green%2C+Shawn+O%E2%80%99Banion%2C+and+Jun+Xie%2E+2025%2E+User%2Dllm%3A+Efficient+llm+contextualization+with+user+embeddings%2E+In+Compan)
27. Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582–… [find](https://scholar.google.com/scholar?q=Xiang+Lisa+Li+and+Percy+Liang%2E+2021%2E+Prefix%2Dtuning%3A+Optimizing+continuous+prompts+for+generation%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+11t)
28. Xinyu Li, Zachary C Lipton, and Liu Leqi. 2024. Personalized language modeling from personalized human feedback. arXiv preprint arXiv:2402.05133. — [arXiv:2402.05133](https://arxiv.org/abs/2402.05133) [find](https://scholar.google.com/scholar?q=Xinyu+Li%2C+Zachary+C+Lipton%2C+and+Liu+Leqi%2E+2024%2E+Personalized+language+modeling+from+personalized+human+feedback%2E+arXiv+preprint+arXiv%3A2402%2E05133%2E+%E2%80%94+%5BarXiv%3A2402%2E05133%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2402%2E05133%29)
29. Damilola Omitaomu, Shabnam Tafreshi, Tingting Liu, Sven Buechel, Chris Callison-Burch, Johannes Eichstaedt, Lyle Ungar, and João Sedoc. 2022. Empathic conversations: A multi-level dataset of contextualized conversations. arXiv preprint arXiv:2205.12698. — [arXiv:2205.12698](https://arxiv.org/abs/2205.12698) [find](https://scholar.google.com/scholar?q=Damilola+Omitaomu%2C+Shabnam+Tafreshi%2C+Tingting+Liu%2C+Sven+Buechel%2C+Chris+Callison%2DBurch%2C+Johannes+Eichstaedt%2C+Lyle+Ungar%2C+and+Jo%C3%A3o+Sedoc%2E+2022%2E+Empathic+conversations%3A+A+multi%2Dlevel+dataset+of+contextua)
30. Chanwoo Park, Mingyang Liu, Kaiqing Zhang, and Asuman Ozdaglar. 2024. Principled rlhf from heterogeneous feedback via personalization and preference aggregation. arXiv preprint arXiv:2405.00254. — [arXiv:2405.00254](https://arxiv.org/abs/2405.00254) [find](https://scholar.google.com/scholar?q=Chanwoo+Park%2C+Mingyang+Liu%2C+Kaiqing+Zhang%2C+and+Asuman+Ozdaglar%2E+2024%2E+Principled+rlhf+from+heterogeneous+feedback+via+personalization+and+preference+aggregation%2E+arXiv+preprint+arXiv%3A2405%2E00254%2E+%E2%80%94+%5Bar)
31. Zhiyuan Liang, Dongwen Tang, Yuhao Zhou, Xuanlei Zhao, Mingjia Shi, Wangbo Zhao, Zekai Li, Peihao Wang, Konstantin Schürholt, Damian Borth, and 1 others. 2025. Drag-and-drop llms: Zero-shot prompt-to-weights. arXiv preprint arXiv:2506.16406. — [arXiv:2506.16406](https://arxiv.org/abs/2506.16406) [find](https://scholar.google.com/scholar?q=Zhiyuan+Liang%2C+Dongwen+Tang%2C+Yuhao+Zhou%2C+Xuanlei+Zhao%2C+Mingjia+Shi%2C+Wangbo+Zhao%2C+Zekai+Li%2C+Peihao+Wang%2C+Konstantin+Sch%C3%BCrholt%2C+Damian+Borth%2C+and+1+others%2E+2025%2E+Drag%2Dand%2Ddrop+llms%3A+Zero%2Dshot+prompt%2Dto%2D)
32. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, and 1 others. 2019. [Pytorch: An imperative style, high-performance deep learning library](https://md.research.elamur.ai/free/en/papers/paszke-2019-pytorch-imperative-style-high-performance/). Advances in neural information processing systems, 32.
33. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
34. Jason Phang, Yi Mao, Pengcheng He, and Weizhu Chen. 2023. Hypertuning: Toward adapting large language models without back-propagation. In International Conference on Machine Learning, pages 27854–27875. pmlr. [find](https://scholar.google.com/scholar?q=Jason+Phang%2C+Yi+Mao%2C+Pengcheng+He%2C+and+Weizhu+Chen%2E+2023%2E+Hypertuning%3A+Toward+adapting+large+language+models+without+back%2Dpropagation%2E+In+International+Conference+on+Machine+Learning%2C+pages+27854%E2%80%932787)
35. Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. 2022. [Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/). Advances in Neural Information Processing Systems, 35:1950–1965.
36. Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, and Abhinav Sethy. 2023. Integrating summarization and retrieval for enhanced personalization via large language models. arXiv preprint arXiv:2310.20081. — [arXiv:2310.20081](https://arxiv.org/abs/2310.20081) [find](https://scholar.google.com/scholar?q=Chris+Richardson%2C+Yao+Zhang%2C+Kellen+Gillespie%2C+Sudipta+Kar%2C+Arshdeep+Singh%2C+Zeynab+Raeesy%2C+Omar+Zia+Khan%2C+and+Abhinav+Sethy%2E+2023%2E+Integrating+summarization+and+retrieval+for+enhanced+personalization+)
37. Jiahong Liu, Zexuan Qiu, Zhongyang Li, Quanyu Dai, Jieming Zhu, Minda Hu, Menglin Yang, and Irwin King. 2025. A survey of personalized large language models: Progress and future directions. arXiv preprint arXiv:2502.11528. — [arXiv:2502.11528](https://arxiv.org/abs/2502.11528) [find](https://scholar.google.com/scholar?q=Jiahong+Liu%2C+Zexuan+Qiu%2C+Zhongyang+Li%2C+Quanyu+Dai%2C+Jieming+Zhu%2C+Minda+Hu%2C+Menglin+Yang%2C+and+Irwin+King%2E+2025%2E+A+survey+of+personalized+large+language+models%3A+Progress+and+future+directions%2E+arXiv+prep)
38. Alireza Salemi, Surya Kallumadi, and Hamed Zamani. 2024a. Optimization methods for personalizing large language models through retrieval augmentation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, J… [find](https://scholar.google.com/scholar?q=Alireza+Salemi%2C+Surya+Kallumadi%2C+and+Hamed+Zamani%2E+2024a%2E+Optimization+methods+for+personalizing+large+language+models+through+retrieval+augmentation%2E+In+Proceedings+of+the+47th+International+ACM+SIGI)
39. Chuancheng Lv, Lei Li, Shitou Zhang, Gang Chen, Fanchao Qi, Ningyu Zhang, and Hai-Tao Zheng. 2024. Hyperlora: Efficient cross-task generalization via constrained low-rank adapters generation. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16376–16393. [find](https://scholar.google.com/scholar?q=Chuancheng+Lv%2C+Lei+Li%2C+Shitou+Zhang%2C+Gang+Chen%2C+Fanchao+Qi%2C+Ningyu+Zhang%2C+and+Hai%2DTao+Zheng%2E+2024%2E+Hyperlora%3A+Efficient+cross%2Dtask+generalization+via+constrained+low%2Drank+adapters+generation%2E+In+Findi)
40. Alireza Salemi, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Weize Kong, Tao Chen, Zhuowan Li, Michael Bendersky, and Hamed Zamani. 2025. Reasoning-enhanced self-training for long-form personalized text generation. arXiv preprint arXiv:2501.04167. 23567 11 — [arXiv:2501.04167](https://arxiv.org/abs/2501.04167) [find](https://scholar.google.com/scholar?q=Alireza+Salemi%2C+Cheng+Li%2C+Mingyang+Zhang%2C+Qiaozhu+Mei%2C+Weize+Kong%2C+Tao+Chen%2C+Zhuowan+Li%2C+Michael+Bendersky%2C+and+Hamed+Zamani%2E+2025%2E+Reasoning%2Denhanced+self%2Dtraining+for+long%2Dform+personalized+text+gen)
41. Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. 2024b. [LaMP: When large language models meet personalization](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7370–7392, Bangkok, Thailand. Association for…
42. Alireza Salemi and Hamed Zamani. 2025. [Lamp-qa: A benchmark for personalized long-form question answering](https://md.research.elamur.ai/free/en/papers/salemi-2025-lamp-qa-benchmark-personalized-long/). arXiv preprint arXiv:2506.00137.
43. Idan Shenfeld, Felix Faltings, Pulkit Agrawal, and Aldo Pacchiano. 2025. Language model personalization via reward factorization. arXiv preprint arXiv:2503.06358. — [arXiv:2503.06358](https://arxiv.org/abs/2503.06358) [find](https://scholar.google.com/scholar?q=Idan+Shenfeld%2C+Felix+Faltings%2C+Pulkit+Agrawal%2C+and+Aldo+Pacchiano%2E+2025%2E+Language+model+personalization+via+reward+factorization%2E+arXiv+preprint+arXiv%3A2503%2E06358%2E+%E2%80%94+%5BarXiv%3A2503%2E06358%5D%28https%3A%2F%2Farxiv%2Eor)
44. Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023. [Large language models can be easily distracted by irrelevant context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/). In International Conference on Machine Learning, pages 31210–31227. PMLR.
45. Robin Staab, Mark Vero, Mislav Balunovic, and Martin Vechev. 2023. Beyond memorization: Violating privacy via inference with large language models. In The Twelfth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Robin+Staab%2C+Mark+Vero%2C+Mislav+Balunovic%2C+and+Martin+Vechev%2E+2023%2E+Beyond+memorization%3A+Violating+privacy+via+inference+with+large+language+models%2E+In+The+Twelfth+International+Conference+on+Learning+)
46. Chenkai Sun, Ke Yang, Revanth Gangi Reddy, Yi Ren Fung, Hou Pong Chan, Kevin Small, ChengXiang Zhai, and Heng Ji. 2025. [Persona-db: Efficient large language model personalization for response prediction with collaborative data refinement](https://md.research.elamur.ai/free/en/papers/sun-2025-persona-db-efficient-large-language/). In Proceedings of the 31st International Conference on Comput…
47. Zhaoxuan Tan and Meng Jiang. 2023. User modeling in the era of large language models: Current research and future directions. IEEE Data Eng. Bull., 47(4):57–96. [find](https://scholar.google.com/scholar?q=Zhaoxuan+Tan+and+Meng+Jiang%2E+2023%2E+User+modeling+in+the+era+of+large+language+models%3A+Current+research+and+future+directions%2E+IEEE+Data+Eng%2E+Bull%2E%2C+47%284%29%3A57%E2%80%9396%2E)
48. Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, and Meng Jiang. 2025a. Aligning large language models with implicit preferences from user-generated content. In Proceedings of the 63rd Annual Meeting of… [find](https://scholar.google.com/scholar?q=Zhaoxuan+Tan%2C+Zheng+Li%2C+Tianyi+Liu%2C+Haodong+Wang%2C+Hyokun+Yun%2C+Ming+Zeng%2C+Pei+Chen%2C+Zhihan+Zhang%2C+Yifan+Gao%2C+Ruijie+Wang%2C+Priyanka+Nigam%2C+Bing+Yin%2C+and+Meng+Jiang%2E+2025a%2E+Aligning+large+language+models)
49. Zhaoxuan Tan, Zheyuan Liu, and Meng Jiang. 2024a. [Personalized pieces: Efficient personalized large language models through collaborative efforts](https://md.research.elamur.ai/free/en/papers/tan-2024-personalized-pieces-efficient-personalized-large/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024, pages 6459–…
50. Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, and Meng Jiang. 2024b. [Democratizing large language models via personalized parameter-efficient fine-tuning](https://md.research.elamur.ai/free/en/papers/tan-2024-democratizing-large-language-models-personalized/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November…
51. Zhaoxuan Tan, Zinan Zeng, Qingkai Zeng, Zhenyu Wu, Zheyuan Liu, Fengran Mo, and Meng Jiang. 2025b. Can large language models understand preferences in personalized recommendation? CoRR, abs/2501.13391. — [arXiv:2501.13391](https://arxiv.org/abs/2501.13391) [find](https://scholar.google.com/scholar?q=Zhaoxuan+Tan%2C+Zinan+Zeng%2C+Qingkai+Zeng%2C+Zhenyu+Wu%2C+Zheyuan+Liu%2C+Fengran+Mo%2C+and+Meng+Jiang%2E+2025b%2E+Can+large+language+models+understand+preferences+in+personalized+recommendation%3F+CoRR%2C+abs%2F2501%2E13391)
52. Andrew Trotman, Antti Puurula, and Blake Burgess. 2014. Improvements to bm25 and language models examined. In Proceedings of the 19th Australasian Document Computing Symposium, pages 58–65. [find](https://scholar.google.com/scholar?q=Andrew+Trotman%2C+Antti+Puurula%2C+and+Blake+Burgess%2E+2014%2E+Improvements+to+bm25+and+language+models+examined%2E+In+Proceedings+of+the+19th+Australasian+Document+Computing+Symposium%2C+pages+58%E2%80%9365%2E)
53. Danqing Wang, Kevin Yang, Hanlin Zhu, Xiaomeng Yang, Andrew Cohen, Lei Li, and Yuandong Tian. 2024. Learning personalized alignment for evaluating open-ended text generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, Novemb… [find](https://scholar.google.com/scholar?q=Danqing+Wang%2C+Kevin+Yang%2C+Hanlin+Zhu%2C+Xiaomeng+Yang%2C+Andrew+Cohen%2C+Lei+Li%2C+and+Yuandong+Tian%2E+2024%2E+Learning+personalized+alignment+for+evaluating+open%2Dended+text+generation%2E+In+Proceedings+of+the+202)
54. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and 1 others. 2020. [Transformers: State-of-the-art natural language processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/). In Proceedings of the 2020 conference on empirical methods in natural lan…
55. Shujin Wu, Yi R. Fung, Cheng Qian, Jeonghwan Kim, Dilek Hakkani-Tur, and Heng Ji. 2025. [Aligning LLMs with individual preferences via interaction](https://md.research.elamur.ai/free/en/papers/wu-2025-aligning-llms-individual-preferences-interaction/). In Proceedings of the 31st International Conference on Computational Linguistics, pages 7648–7662, Abu Dhabi, UAE. Association for Computational Linguist…
56. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, and 22 others. 2024. Qwen2.5 technical report. CoRR, abs/2412.1511… [find](https://scholar.google.com/scholar?q=An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+Huan+Lin%2C+Jian+Yang%2C+Jianhong+Tu%2C+Jianwei+Zhang%2C+Jianxin+Yang%2C+Jiaxi+Yang%2C+Jin)
57. 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. [find](https://scholar.google.com/scholar?q=Jiarui+Zhang%2E+2024%2E+Guided+profile+generation+improves+personalization+with+large+language+models%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2C+pages+4005%E2%80%934016%2C+Miami%2C+Flo)
58. Linhai Zhang, Jialong Wu, Deyu Zhou, and Yulan He. 2025a. [PROPER: A progressive learning framework for personalized large language models with group-level adaptation](https://md.research.elamur.ai/free/en/papers/zhang-2025-proper-progressive-learning-framework-personalized/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16399– 1641…
59. Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, Fei Huang, and Jingren Zhou. 2025b. [Qwen3 embedding: Advancing text embedding and reranking through foundation models](https://md.research.elamur.ai/free/en/papers/zhang-2025-qwen3-embedding-advancing-text/). CoRR, abs/2506.05176.
60. Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Ju-Ying Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, and 2 others. 2024. Personalization of large… [find](https://scholar.google.com/scholar?q=Zhehao+Zhang%2C+Ryan+A%2E+Rossi%2C+Branislav+Kveton%2C+Yijia+Shao%2C+Diyi+Yang%2C+Hamed+Zamani%2C+Franck+Dernoncourt%2C+Joe+Barrow%2C+Tong+Yu%2C+Sungchul+Kim%2C+Ruiyi+Zhang%2C+Jiuxiang+Gu%2C+Tyler+Derr%2C+Hongjie+Chen%2C+Ju%2DYing+W)
61. Zheng Zhao, Clara Vania, Subhradeep Kayal, Naila Khan, Shay B. Cohen, and Emine Yilmaz. 2025. Per-sonalens: A benchmark for personalization evaluation in conversational AI assistants. In Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025, p… [find](https://scholar.google.com/scholar?q=Zheng+Zhao%2C+Clara+Vania%2C+Subhradeep+Kayal%2C+Naila+Khan%2C+Shay+B%2E+Cohen%2C+and+Emine+Yilmaz%2E+2025%2E+Per%2Dsonalens%3A+A+benchmark+for+personalization+evaluation+in+conversational+AI+assistants%2E+In+Findings+of+t)
62. Aakas Zhiyuli, Yanfang Chen, Xuan Zhang, and Xun Liang. 2023. Bookgpt: A general framework for book recommendation empowered by large language model. arXiv preprint arXiv:2305.15673. — [arXiv:2305.15673](https://arxiv.org/abs/2305.15673) [find](https://scholar.google.com/scholar?q=Aakas+Zhiyuli%2C+Yanfang+Chen%2C+Xuan+Zhang%2C+and+Xun+Liang%2E+2023%2E+Bookgpt%3A+A+general+framework+for+book+recommendation+empowered+by+large+language+model%2E+arXiv+preprint+arXiv%3A2305%2E15673%2E+%E2%80%94+%5BarXiv%3A2305%2E156)
63. Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, and Bo Dai. 2024. HYDRA: model factorization framework for black-box LLM personalization. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, V… [find](https://scholar.google.com/scholar?q=Yuchen+Zhuang%2C+Haotian+Sun%2C+Yue+Yu%2C+Rushi+Qiang%2C+Qifan+Wang%2C+Chao+Zhang%2C+and+Bo+Dai%2E+2024%2E+HYDRA%3A+model+factorization+framework+for+black%2Dbox+LLM+personalization%2E+In+Advances+in+Neural+Information+Pro)

*Updated 1 November 2026*