2025-01-14-he25a.md

title	Enhancing Textbook Question Answering with Knowledge Graph-Augmented Large Language Models
booktitle	Proceedings of the 16th Asian Conference on Machine Learning
year	2025
volume	260
series	Proceedings of Machine Learning Research
month	0
publisher	PMLR
pdf	https://raw.githubusercontent.com/mlresearch/v260/main/assets/he25a/he25a.pdf
url	https://proceedings.mlr.press/v260/he25a.html
openreview	ATiIqCCqR2
abstract	Previous works on Textbook Question Answering suffer from limited performance due to the small-scale neural network based backbone. To alleviate the issue, we propose to utilize LLMs as the backbone of TQA tasks. To this end, we utilize two methods, the raw-context based prompting method and the knowledge graph based prompting method. Specifically, we introduce the Textbook Question Answering-Knowledge Graph (TQA-KG) method, which first converts textbook content into structural knowledge graphs and then combining knowledge graph into LLM prompting, thereby enhancing the model’s reasoning capabilities and answer accuracy. Extensive experiments conducted on the CK12-QA dataset illustrate the effectiveness of the method, achieving an improvement of 5.67% in accuracy compared to current state-of-the-art methods on average.
layout	inproceedings
issn	2640-3498
id	he25a
tex_title	Enhancing Textbook Question Answering with Knowledge Graph-Augmented Large Language Models
firstpage	639
lastpage	654
page	639-654
order	639
cycles	false
bibtex_editor	Nguyen, Vu and Lin, Hsuan-Tien
editor	given family Vu Nguyen given family Hsuan-Tien Lin
bibtex_author	He, Mengliang and Zhou, Aimin and Shi, Xiaoming
author	given family Mengliang He given family Aimin Zhou given family Xiaoming Shi
date	2025-01-14
address
container-title	Proceedings of the 16th Asian Conference on Machine Learning
genre	inproceedings
issued	date-parts 2025 1 14
extras