# Enhancing textual textbook question answering with large language models and retrieval augmented generation

arXiv: https://arxiv.org/abs/2402.05128 · HTML: https://mlsanity.com/paper/2402.05128/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family](https://mlsanity.com/paper/2303.07992/) (2023)
- [Multi-Hop Paragraph Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/1906.06606/) (2019)
- [Improving Retrieval-Augmented Large Language Models via Data Importance Learning](https://mlsanity.com/paper/2307.03027/) (2023)
- [Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence](https://mlsanity.com/paper/2211.06318/) (2022)
- [Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model](https://mlsanity.com/paper/2305.17116/) (2023)
- [ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention](https://mlsanity.com/paper/2010.00562/) (2020)
- [XTQA: Span-Level Explanations of the Textbook Question Answering](https://mlsanity.com/paper/2011.12662/) (2020)

## Built on by (most cited first, 14 of 14)

- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization](https://mlsanity.com/paper/2410.09302/) (2024)
- [Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization](https://mlsanity.com/paper/2412.18279/) (2024)
- [Corvid: Improving Multimodal Large Language Models Towards Chain-of-Thought Reasoning](https://mlsanity.com/paper/2507.07424/) (2025)
- [MELLM: A Flow-Guided Large Language Model for Micro-Expression Understanding](https://mlsanity.com/paper/2505.07007/) (2025)
- [Enhancing Frame Detection with Retrieval Augmented Generation](https://mlsanity.com/paper/2502.12210/) (2025)
- [Evaluating Multimodal Large Language Models on Educational Textbook Question Answering](https://mlsanity.com/paper/2506.21596/) (2025)
- [Physical Plausibility Reasoning via HCM-GRPO: Empowering Compact Model for Superior Performance](https://mlsanity.com/paper/2511.10055/) (2025)
- [Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering](https://mlsanity.com/paper/2505.13520/) (2025)
- [Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making](https://mlsanity.com/paper/2508.16420/) (2025)
- [ALIVE: An Avatar-Lecture Interactive Video Engine with Content-Aware Retrieval for Real-Time Interaction](https://mlsanity.com/paper/2512.20858/) (2025)
- [Towards Privacy-Preserving Mental Health Support with Large Language Models](https://mlsanity.com/paper/2601.01993/) (2026)
- [TeachMateGPT: A Multi-Agent Knowledge-Grounded Framework for Pedagogical Assessment Generation from Science Curriculum Materials](https://mlsanity.com/paper/2608.13708/) (2026)
- [Learning Adaptive Deliberation Control for Reliable Recognition with Multi-Agent Language Models](https://mlsanity.com/paper/2610.17840/) (2026)

## Abstract (arXiv)

Textbook question answering (TQA) is a challenging task in artificial intelligence due to the complex nature of context needed to answer complex questions. Although previous research has improved the task, there are still some limitations in textual TQA, including weak reasoning and inability to capture contextual information in the lengthy context. We propose a framework (PLRTQA) that incorporates the retrieval augmented generation (RAG) technique to handle the out-of-domain scenario where concepts are spread across different lessons, and utilize transfer learning to handle the long context and enhance reasoning abilities. Our architecture outperforms the baseline, achieving an accuracy improvement of 4. 12% in the validation set and 9. 84% in the test set for textual multiple-choice questions. While this paper focuses on solving challenges in the textual TQA, It provides a foundation for future work in multimodal TQA where the visual components are integrated to address more complex educational scenarios. Code: https://github.com/hessaAlawwad/PLR-TQA