arXiv:2605.24052v1 Announce Type: new 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 preference feedback to maximize their influence or payment. Existing pipelines in mobile crowdsourcing (e.g., EM-based weight estimation) fail to identify the most accurate worker in this onli
The proliferation of LLMs in mobile applications necessitates robust, truthful feedback mechanisms to ensure model alignment and performance, making this research timely.
This research addresses a critical challenge in fine-tuning LLMs with human feedback in crowdsourcing, proposing methods to counter strategic misreporting and ensure data integrity.
The ability to more reliably aggregate human preferences for LLM fine-tuning in crowdsourced environments improves model accuracy and trustworthiness, enabling broader deployment.
- · Mobile crowdsourcing platforms
- · Developers of AI-driven mobile applications
- · Users of LLM-powered services
- · Malicious or strategic misreporters
- · Inefficient preference aggregation methods
Improved accuracy and utility of LLMs integrated into mobile applications.
Increased trust in AI-generated content and predictions, leading to wider adoption in critical sectors.
The development of more sophisticated and resilient crowdsourcing and AI feedback loops across various industries.
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