aiqbench analysis
This research identifies "personalization collapse," a failure mode where explicit style instructions in large language models inadvertently erode user-specific characteristics. The authors propose PsPLUG, a lightweight plug-in that learns user residuals to maintain personalization while allowing precise control over style adherence at inference time. This work offers a critical advancement for deploying customized LLMs that must simultaneously respect both individual user preferences and explicit stylistic constraints.
Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs
arXiv:2601.06362v3 Announce Type: replace Abstract: Personalized large language models are often expected to follow explicit style instructions, yet we find that such instructions can undermine the user-specific characteristics that personalization methods aim to preserve. We call this failure mode personalization collapse: explicit style control can conflict with implicit user preferences. To address this challenge, we propose PsPLUG, a lightweight plug-in that learns a user-specific residual after accounting for the requested style. PsPLUG also allows us to tune personalization strength at inference time. Our experiments show that explicit style instructions can diminish personalization in existing methods, whereas PsPLUG better preserves user preferences while providing precise control over the balance between personalization and style adherence.
Title:Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMs
Abstract:Personalized large language models are often expected to follow explicit style instructions, yet we find that such instructions can undermine the user-specific characteristics that personalization methods aim to preserve. We call this failure mode personalization collapse: explicit style control can conflict with implicit user preferences. To address this challenge, we propose PsPLUG, a lightweight plug-in that learns a user-specific residual after accounting for the requested style. PsPLUG also allows us to tune personalization strength at inference time. Our experiments show that explicit style instructions can diminish personalization in existing methods, whereas PsPLUG better preserves user preferences while providing precise control over the balance between personalization and style adherence.
Artificial Intelligence (cs.AI)
arXiv:2601.06362 [cs.AI]
(or arXiv:2601.06362v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2601.06362 Focus to learn more arXiv-issued DOI via DataCite
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