Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing 事件

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing arXiv:2605.24052v1 Announce Type: cross Abstract: To better serve users' demands in mobile applications (e.g., navigation), mobile crowdsourcing platforms can iteratively align large language model (LLM)-generated content (e.g., AI-generated traffic condition predictions) with human feedback collected from crowdsourcing workers (e.g., mobile users). However, workers may strategically misreport their online prefer