Volunteered geographic information (VGI) platforms often suffer from redundant submissions, as contributors tend to create new reports rather than update existing ones—partly due to the effort required to locate and examine relevant prior entries. This study introduces Tell2Find, a large language model (LLM)-powered agent designed to support report retrieval and reuse during mobile crowdsourcing tasks. We conducted a study comparing Tell2Find with a traditional filter-based tool and a hybrid version combining both. Users with access to Tell2Find were more likely to examine existing reports before acting, resulting in more updates to prior reports and lower redundancy. Qualitatively, participants highlighted Tell2Find’s advantages in reducing the effort of matching reports, improving convenience in mobile contexts, and encouraging engagement with prior data due to more relevant suggestions. However, we also observed friction points: user trust around the system’s opaque matching logic and uncertainty about input phrasing occasionally led to disengagement with Tell2Find.

This three-phase study explores the experiential background of contributors to platforms that provide crowdsourced location-related information. Initially, we utilized interviews to understand users’ expectations for location-related information and the contributors’ experiential background they believe would enhance this information’s utility. We then deployed a survey to identify the top eight sought-after location-information types and their perceived characteristics. Then the concluding online scenario-based study provided quantitative evidence about the interrelationships of eight types of location-related information, ten crucial quality attributes, and aspects of the contributors’ experiential background believed to enhance the utility of the descriptions they provide. Notably, although certain experiential background aspects were deemed universally advantageous across all information types, unique connections were identified among specific information types and distinct experiential background aspects seen as augmenting the contributor’s descriptions’ utility. These insights underline the importance of location-based crowdsourcing platforms incorporating contributors’ experiential background when assigning tasks.