Multimodal Data Fusion for Tabular and Textual Data: Zero-Shot, Few-Shot, and Fine-Tuning of Generative Pre-Trained Transformer Models

Multimodal Data Fusion for Tabular and Textual Data: Zero-Shot, Few-Shot, and Fine-Tuning of Generative Pre-Trained Transformer Models

In traffic safety analysis, previous research has often focused on tabular data or textual crash narratives in isolation, neglecting the potential benefits of a hybrid multimodal approach. This study introduces the Multimodal Data Fusion (MDF) framework, which fuses tabular data with textual narrati...

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Bibliographic Details
Main Authors: Shadi Jaradat, Mohammed Elhenawy, Richi Nayak, Alexander Paz, Huthaifa I. Ashqar, Sebastien Glaser
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:AI
Subjects:
Generative Pre-Trained Transformer (GPT)
Large Language Model (LLM)
traffic crash analysis
few-shot learning
zero-shot learning
Multimodal Data Fusion
Online Access:https://www.mdpi.com/2673-2688/6/4/72
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