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

This paper investigates truthful online preference aggregation for fine-tuning Large Language Models (LLMs) in mobile crowdsourcing. It proposes a novel online weighted aggregation mechanism to address strategic misreporting by workers, modeling the process as a dynamic Bayesian game. The goal is to overcome existing approaches that fail to identify the most accurate worker and result in linear regret.