RESEARCHMonitorWATCHLIST
Over-Personalization Is a Decision Failure: Generation-Induced Apply Bias in LLMs
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
arXiv research identifies 'apply bias' in personalized LLMs, showing models over-apply stored user preferences when contexts rule them out.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 1 October 2026
- Collected by OneBench
- 2 Oct 2026, 03:01 UK
Stored source excerpt
arXiv:2609.34284v2 Announce Type: replace Abstract: Personalized LLMs must decide, for each stored preference, whether the current context calls for applying or suppressing it, which we…
Short excerpt from the collected text, not the full source. Use the source link to read it in context.
The factual summary is a OneBench synthesis, not a quotation or independent verification. Collection time is not publication time. Open the source for its full context; related reporting can share the same underlying announcement.