Incorporating Citizen-Generated Data into Large Language Models

Jagadeesh Vadapalli, Srishti Gupta, Bishwa Karki, Chun Hua Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This study investigates the use of citizen-generated data to optimize a large language model (LLM) chatbot that gives nutrition advice. By actively participating in the data collection and annotation process from FDA-approved websites, citizens provided insightful information that was essential for improving the model and addressing biases. The study highlights the difficulties in gathering and annotating data, especially in situations where nuances matter, such as pregnancy nutrition. The results show that the use of citizen-generated data improves the efficacy and efficiency of data collection procedures, providing a practical viewpoint and encouraging community involvement. In addition to guaranteeing data quality, the iterative process raises stakeholders’ awareness of and proficiency with data. Thus, citizen-generated data becomes an essential tool for creating information systems that are more reliable and inclusive.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th Annual International Conference on Digital Government Research, DGO 2024
EditorsHsin-Chung Liao, David Duenas Cid, Marie Anne Macadar, Flavia Bernardini
PublisherAssociation for Computing Machinery
Pages1023-1025
Number of pages3
ISBN (Electronic)9798400709883
DOIs
StatePublished - Jun 11 2024
Event25th Annual International Conference on Digital Government Research, DGO 2024 - Taipei, Taiwan, Province of China
Duration: Jun 11 2024Jun 14 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference25th Annual International Conference on Digital Government Research, DGO 2024
Country/TerritoryTaiwan, Province of China
CityTaipei
Period6/11/246/14/24

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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