Disaster Question Answering with LoRA Efficiency and Accurate End Position

TL;DR

This paper presents a disaster-focused question answering system using Japanese BERT, Bi-LSTM, and LoRA optimization, achieving high accuracy with minimal parameters for effective disaster response support.

Contribution

It introduces a novel disaster Q&A system leveraging LoRA efficiency and combines Japanese BERT with Bi-LSTM for accurate, lightweight disaster information retrieval.

Findings

Achieved 70.4% End Position accuracy with only 5.7% of total parameters.

Attained a 0.885 Span F1 score suitable for real disaster scenarios.

Demonstrated the effectiveness of Japanese BERT and Bi-LSTM combination for disaster Q&A.

Abstract

Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals face disaster situations, they often experience confusion and lack the domain-specific knowledge and experience necessary to determine appropriate responses and actions. While disaster information is continuously updated, even when utilizing RAG search and large language models for inquiries, obtaining relevant domain knowledge about natural disasters and experiences similar to one's specific situation is not guaranteed. When hallucinations are included in disaster question answering, artificial misinformation may spread and exacerbate confusion. This work introduces a disaster-focused question answering system based on Japanese disaster situations and response experiences. Utilizing the cl-tohoku/bert-base-japanese-v3 + Bi-LSTM + Enhanced Position Heads architecture with LoRA efficiency optimization, we achieved 70.4\% End Position accuracy with only 5.7\% of the total parameters (6.7M/117M). Experimental results demonstrate that the combination of Japanese BERT-base optimization and Bi-LSTM contextual understanding achieves accuracy levels suitable for real disaster response scenarios, attaining a 0.885 Span F1 score. Future challenges include: establishing natural disaster Q\&A benchmark datasets, fine-tuning foundation models with disaster knowledge, developing lightweight and power-efficient edge AI Disaster Q\&A applications for situations with insufficient power and communication during disasters, and addressing disaster knowledge base updates and continual learning capabilities.