Summary of Enhancing Textual Textbook Question Answering with Large Language Models and Retrieval Augmented Generation, by Hessa Abdulrahman Alawwad et al.
Enhancing textual textbook question answering with large language models and retrieval augmented generation
by Hessa Abdulrahman Alawwad, Areej Alhothali, Usman Naseem, Ali Alkhathlan, Amani Jamal
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework called PLRTQA to tackle the challenging task of textbook question answering (TQA). The approach incorporates retrieval-augmented generation (RAG) to handle out-of-domain scenarios and utilizes transfer learning to enhance reasoning abilities. By leveraging these techniques, the architecture achieves an accuracy improvement of 4.12% in the validation set and 9.84% in the test set for textual multiple-choice questions. This work provides a foundation for future research in multimodal TQA, where visual components are integrated to address more complex educational scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better answer questions from textbooks by using a special kind of artificial intelligence. The problem is that traditional approaches don’t always understand the context needed to give good answers. This new framework, called PLRTQA, uses two key techniques: one to help find relevant information and another to improve reasoning skills. By combining these ideas, the approach does better than others in answering questions from textbooks. This research can be used as a starting point for even more advanced work that combines text with images or videos. |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation » Transfer learning
