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Self-supervised User Profile Generation for Personalization

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arXiv:2606. 05336v1 Announce Type: new Abstract: Personalizing large language models (LLMs) has become a central challenge as LLMs are deployed across recommendation, search, dialogue, and content generation -- settings where the same query should yield different answers given different users.

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Authors: Clark Mingxuan Ju, Yuwei Qiu, Tong Zhao, Neil Shah
Released: 2026-06-05 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Self-supervised User Profile Generation for Personalization overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, persona, rect, under, eval, source, rate, without. Source: arxiv cs.CL (NLP).
arXiv: 2606.05336

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