  Textbook question answering (TQA) is a challenging task in artificial
intelligence due to the complex nature of context and multimodal data. Although
previous research has significantly improved the task, there are still some
limitations including the models' weak reasoning and inability to capture
contextual information in the lengthy context. The introduction of large
language models (LLMs) has revolutionized the field of AI, however, directly
applying LLMs often leads to inaccurate answers. This paper proposes a
methodology that handle the out-of-domain scenario in TQA where concepts are
spread across different lessons by incorporating the retrieval augmented
generation (RAG) technique and utilize transfer learning to handle the long
context and enhance reasoning abilities. Through supervised fine-tuning of the
LLM model Llama-2 and the incorporation of RAG, our architecture outperforms
the baseline, achieving a 4.12% accuracy improvement on validation set and
9.84% on test set for non-diagram multiple-choice questions.
