# Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2504.05276 · HTML: https://mlsanity.com/paper/2504.05276/

## Key facts

- Cited by 7 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 5 of its 19 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2402.16893/) (2024)
- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges](https://mlsanity.com/paper/2401.08664/) (2023)

## Built on by (most cited first, 7 of 7)

- [Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs](https://mlsanity.com/paper/2510.07484/) (2025)
- [Confusion-Aware Rubric Optimization for LLM-based Automated Grading](https://mlsanity.com/paper/2603.00451/) (2026)
- [Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study](https://mlsanity.com/paper/2509.07846/) (2025)
- [LLM-as-a-Grader: Practical Insights from Large Language Model for Short-Answer and Report Evaluation](https://mlsanity.com/paper/2511.10819/) (2025)
- [CHiL(L)Grader: Calibrated Human-in-the-Loop Short-Answer Grading](https://mlsanity.com/paper/2603.11957/) (2026)
- [From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG](https://mlsanity.com/paper/2603.19276/) (2026)
- [The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty](https://mlsanity.com/paper/2607.26067/) (2026)

## Abstract (arXiv)

Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that possess human-like ability in linguistic tasks are increasingly popular in assisting human graders to reduce their workload. However, LLMs' limitations in domain knowledge restrict their understanding in task-specific requirements and hinder their ability to achieve satisfactory performance. Retrieval-augmented generation (RAG) emerges as a promising solution by enabling LLMs to access relevant domain-specific knowledge during assessment. In this work, we propose an adaptive RAG framework for automated grading that dynamically retrieves and incorporates domain-specific knowledge based on the question and student answer context. Our approach combines semantic search and curated educational sources to retrieve valuable reference materials. Experimental results in a science education dataset demonstrate that our system achieves an improvement in grading accuracy compared to baseline LLM approaches. The findings suggest that RAG-enhanced grading systems can serve as reliable support with efficient performance gains.