  Large Language Models (LLMs) have recently demonstrated exceptional
performance in various Natural Language Processing (NLP) tasks. They have also
shown the ability to perform chain-of-thought (CoT) reasoning to solve complex
problems. Recent studies have explored CoT reasoning in complex multimodal
scenarios, such as the science question answering task, by fine-tuning
multimodal models with high-quality human-annotated CoT rationales. However,
collecting high-quality COT rationales is usually time-consuming and costly.
Besides, the annotated rationales are hardly accurate due to the external
essential information missed. To address these issues, we propose a novel
method termed T-SciQ that aims at teaching science question answering with LLM
signals. The T-SciQ approach generates high-quality CoT rationales as teaching
signals and is advanced to train much smaller models to perform CoT reasoning
in complex modalities. Additionally, we introduce a novel data mixing strategy
to produce more effective teaching data samples for simple and complex science
question answer problems. Extensive experimental results show that our T-SciQ
method achieves a new state-of-the-art performance on the ScienceQA benchmark,
with an accuracy of 96.18%. Moreover, our approach outperforms the most
powerful fine-tuned baseline by 4.5%. The code is publicly available at
https://github.com/T-SciQ/T-SciQ.
