Collision Care Guide Based on Large Language Models

EasyChair Preprint 15275

Abstract

This study introduces the Collision Clarification Generator (CCG), a Large Language Model-based system designed to assist in documenting traffic accidents. The CCG comprises three modules: Questioning, Information Extraction, and Accident Sequence Generation, which collectively streamline the process of gathering and structuring accident information. The system employs predefined question templates and a standardized Traffic Accident Record Format (TARF) to ensure comprehensive data collection.

Evaluation of the CCG involved both human assessment and LLM-based automatic evaluation. Results showed an F1 score of 0.909 in human evaluation, and scores exceeding 7 out of 10 for accuracy and completeness in LLM-based assessment. These findings demonstrate the CCG's effectiveness in accurately documenting accident information, potentially facilitating subsequent legal and insurance processes.

Keyphrases: 交通事故, 大型語言模型, 對話系統, 資訊擷取

@booklet{EasyChair:15275,
  author    = {Jo-Chi Kung and Chia-Hui Chang and Huai-Hsuan Huang and Kuo-Chun Chien},
  title     = {Collision Care Guide Based on Large Language Models},
  howpublished = {EasyChair Preprint 15275},
  year      = {EasyChair, 2024}}