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Instant Personalized Large Language Model Adaptation via Hypernetwork

Paper · 2026 · Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen et al.

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 MontréalMontreal · Canada68 works · 79,382 citations🏢AmazonSeattle · United States112 works · 40,565 citations🎓University of Notre DameNotre Dame · United States51 works · 36,717 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 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. DOI 10.18653/v1/2026.acl-long.1081

@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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