AI RESEARCH
LocalSUG: City-Preference-Enhanced LLM for Query Suggestion in Local-Life Services
arXiv CS.CL
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ArXi:2603.04946v2 Announce Type: replace In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage systems rely heavily on historical popular queries, limiting their ability to capture long-tail and emerging demand. Although LLMs provide strong semantic generalization, their deployment in local-life services faces three challenges: insufficient city-preference awareness, exposure bias in preference optimization, and strict online latency constraints.