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Aligning LLMs with Individual Preferences via Interaction

Paper · 2025 · Shujin Wu, Yi R. Fung, Cheng Qian et al.

Summary

Most LLM alignment work targets broad norms such as helpfulness, harmlessness and honesty, and ignores that individual users have different, often unstated, preferences. The authors train models to "interact to align": over a multi-turn dialogue, the model gradually infers who the user is and adapts its replies. To get training data, they build separate pools of user profiles, which steer conversation topics, and personalities, which steer conversational style. Both pools grow from hand-written seeds through repeated generation with GPT-4o, and near-duplicates are filtered out by semantic similarity. The two pools are then combined into personas. Four LLMs with distinct roles simulate conversations with these personas and produce tree-structured preference data. That data is used for supervised fine-tuning and reinforcement learning. For evaluation, the authors introduce the ALOE benchmark, where an LLM judge scores how well each turn's reply fits the persona. Mainstream models such as Llama-3 adapt poorly over the course of a conversation, and the proposed training substantially improves this. The work offers a practical recipe for personalization without explicit user profiles.

Key points

  • Persona pool: 3,310 distinct personas, built from 20 manual seed examples per pool.
    • Each round uses 5 sampled few-shot examples.
    • New items are kept only if their maximum semantic similarity to the existing pool is below 0.6.
  • Data generation roles: four LLMs with fixed jobs.
    • A role-playing LLM simulates the user.
    • An induction LLM tracks which persona traits have been revealed so far.
    • A "rejected" LLM answers directly, without personalization.
    • A "preferred" LLM answers using the inferred traits.
    • The result is 3K+ tree-structured multi-turn conversations.
  • ALOE benchmark: 100 curated test cases, with conversations capped at 10 turns.
    • GPT-4o rates each reply on a 1–5 scale.
    • Metrics are Alignment Level (the per-turn average score) and Improvement Rate across turns.
  • Main result: an average relative improvement of 32.0% in personalized alignment over the base models.
  • Judge reliability and availability:
    • Agreement between GPT-4o and three human raters averages a Cohen's Kappa of 0.789.
    • Code and data are released at github.com/ShujinWu-0814/ALOE.

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 Southern CaliforniaLos Angeles · United States162 works · 35,836 citations🎓University of Illinois Urbana-ChampaignUrbana · United States184 works · 69,372 citations

 Fields, topics and concepts

📚 Shelves 6The 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 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Shujin Wu, Yi R. Fung, Cheng Qian, Jeonghwan Kim, Dilek Hakkani-Tur and Heng Ji. 2025. Aligning LLMs with Individual Preferences via Interaction. Proceedings of the 31st International Conference on Computational Linguistics.

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

@inproceedings{wu2025aligning,
  title = {{Aligning LLMs with Individual Preferences via Interaction}},
  author = {Shujin Wu, Yi R. Fung, Cheng Qian, Jeonghwan Kim, Dilek Hakkani-Tur and Heng Ji},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.511/}
}

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