Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning
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Computer Science > Computation and Language
arXiv:2609.00014 (cs)
[Submitted on 11 Aug 2026]
Title:Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning
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Abstract:Persona-driven techniques increasingly adapt large language models (LLMs) to diverse contexts. However, existing methods predominantly rely on rigid, synthetic personas that flatten individual variation, rely on stereotypes, and miss the nuanced signals driving actual human preferences. We introduce profile behavioral grounding, a framework for extracting open-ended, high-fidelity user profiles directly from authentic, anonymized social media posts. We evaluate these profiles across two paradigms: train-time personalization via supervised finetuning (SFT) and non-parametric test-time multi-perspective reasoning. Across complex recommendation and open-ended query benchmarks, behaviorally grounded profiles consistently improve base models and outperform synthetic profile baselines, driving stronger parametric alignment and enabling richer, multifaceted reasoning. Our findings establish open-ended, behavior-derived profiles as a highly diverse and effective foundation for the next generation of personalized language systems. Our code base is available at this https URL.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.00014 [cs.CL] |
| (or arXiv:2609.00014v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.00014
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning, by Yuxuan Li and 2 other authors
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