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Untangling Emotional Threads: Hallucination Networks of Large Language Models

  • SUNY Albany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Generative AI models, known for their capacity to generate intricate and realistic data, have rapidly found applications across various domains. Yet, doubts linger regarding the full scope of their hallucinatory capabilities and the reliability of their outcomes. These concerns underscore the need for rigorous analysis and validation of generative AI models. This study employs network analysis to explore the inherent characteristics of generative AI models, focusing on their deviations and disparities between generated and actual content. Using GPT3.5 and RoBERTa, we analyze tweets, vocabulary, and emotion networks from their outputs. Although network comparison demonstrated hallucination, non-classification, and instability patterns in GPT-3.5 compared to RoBERTa as a baseline, both models exhibit promise and room for improvement.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications
Subtitle of host publicationCOMPLEX NETWORKS 2023 Volume 1
EditorsHocine Cherifi, Luis M. Rocha, Chantal Cherifi, Murat Donduran
PublisherSpringer Science and Business Media Deutschland GmbH
Pages202-214
Number of pages13
ISBN (Print)9783031534676
DOIs
StatePublished - 2024
Event12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023 - Menton, France
Duration: Nov 28 2023Nov 30 2023

Publication series

NameStudies in Computational Intelligence
Volume1141 SCI

Conference

Conference12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023
Country/TerritoryFrance
CityMenton
Period11/28/2311/30/23

Keywords

  • Emotions
  • GPT
  • Generative AI
  • Hallucination
  • RoBERTa

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