[2T1-OS-23] OS-23

[2T1-OS-23-03] Prediction of Human Damage Caused by Bears using Large Language Model

〇Shin Nakamoto¹, Yusuke Fukazawa¹ (1. Sophia University Graduate School)

Keywords:Large Language Model, Human damages caused by bears, BERT, Machine Learning

This study proposes a method for predicting the extent of human damage caused by bears from the situation when one encounter the bear using a large language model. As the situation of encouter, we utilize the date and time, location, sex and age of the victim, number of people at the time of the encounter with the bear. In addition, we textualize past findings on bears' attacks. We consider multiple labels as human damages such as death, serious injury, minor injury and sights. We conducted finetuning of Japanese BERT using the labeled encounter situations as training data. We evaluated proposed method on the human damage data by bears in Hokkaido and some areas in Honshu from 2021 to 2023. We confirmed that the proposed method improved the accuracy compared to machine learning methods.