Korean visual abductive reasoning: AI Language Model’s ability to understand plausibility

• 영문명: Korean visual abductive reasoning: AI Language Model’s ability to understand plausibility
• 발행기관: 경희대학교 언어정보연구소
• 저자명: 한선아(Seonah Han) 원종빈(Jongbin Won) 권은재(Eunjae Kwon) 송상헌(Sanghoun Song)
• 간행물 정보: 『언어연구』제41권 제2호, 283~310쪽, 전체 28쪽
• 주제분류: 인문학 > 언어학
• 파일형태: PDF
• 발행일자: 2024.06.30

국문 초록

Visual abductive reasoning is the logical process of drawing the most plausible hypothesis based on given observations. This ability is fundamental to artificial intelligence because it enables inference from incomplete information. However, little research has been conducted on Korean visual abductive reasoning. To examine the capability of a multimodal language model’s Korean visual abductive reasoning, we set a simple baseline model and analyzed how it numerically estimated the plausibility for all Korean hypothesis sentences through a multiple-choice task. This task was implemented using a simple dual encoder model and the Korean Story Cloze dataset. After fine-tuning with the binary-choice task discriminating the plausible hypothesis from the implausible one, our baseline model shows an accuracy of 79.81%. In multiple-choice task designed to check for the influence of overfitting or annotation artifacts, the model estimated the plausibilities of four options in the order of Groundtruth≃ Plausible>Implausible≫Random. We also conducted experiments to analyze how the model performed Korean visual abductive reasoning. It was observed that the model made little use of the observation before the hypothesis but demonstrated a similar tendency to humans, struggling with data samples which humans also struggle with when evaluating the plausibility of given sentences. Our study sets a research foundation for numerically analyzing and understanding the language models’ visual abductive reasoning ability in the Korean context. It also shows both the potential and limitations of the language model’s Korean visual abductive reasoning ability and provides clues for future research directions.

목차

1. Introduction

2. Theoretical background

3. Methods

4. Results

5. Experiments

6. Discussion

7. Conclusion

References

키워드

language model artificial intelligence visual abductive reasoning Korean visual abductive reasoning plausibility multimodal recognition common sense reasoning