AI RESEARCH
BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback
arXiv CS.CL
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ArXi:2509.21106v2 Announce Type: replace Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cognitive burden compared to traditional search systems. Yet they remain insufficient for fully addressing diverse user needs, which requires recognizing how the same query can reflect different intents across users and delivering information in preferred forms.