mlsanityData to 7 Oct 2026

SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation

arXiv 2410.13293 ↗First posted 17 Oct 2024cs.LG cs.AI cs.IRcited by 5

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

Many students struggle with math word problems (MWPs), often finding it difficult to identify key information and select the appropriate mathematical operations. Schema-based instruction (SBI) is an evidence-based strategy that helps students categorize problems based on their structure, improving problem-solving accuracy. Building on this, we propose a Schema-Based Instruction Retrieval-Augmented Generation (SBI-RAG) framework that incorporates a large language model (LLM). Our approach emphasizes step-by-step reasoning by leveraging schemas to guide solution generation. We evaluate its performance on the GSM8K dataset, comparing it with GPT-4 and GPT-3.5 Turbo, and introduce a "reasoning score" metric to assess solution quality. Our findings suggest that SBI-RAG enhances reasoning clarity and facilitates a more structured problem-solving process potentially providing educational benefits for students.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 31 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

11 of 31 references.

References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  6. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
  7. 2023BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Datasetcited by 828
  8. 2019MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalismscited by 653
  9. 2021Measuring Attribution in Natural Language Generation Modelscited by 253
  10. 2023Progressive-Hint Prompting Improves Reasoning in Large Language Modelscited by 147
  11. 2023Solving Math Word Problems by Combining Language Models With Symbolic Solverscited by 124

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025A Survey on Large Language Models for Mathematical Reasoningcited by 52
  2. 2026Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMscited by 3
  3. 2024An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problemscited by 0
  4. 2025Interpretability Framework for LLMs in Undergraduate Calculuscited by 0
  5. 2026SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematicscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Large Language Models Don't Make Sense of Word Problems. A Scoping Review from a Mathematics Education Perspective
  2. 2026MisEdu-RAG: A Misconception-Aware Dual-Hypergraph RAG for Novice Math Teachers
  3. 2024SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
  4. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference
  5. 2025SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
  6. 2025A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics
  7. 2025LemmaHead: RAG Assisted Proof Generation Using Large Language Models
  8. 2024CoinMath: Harnessing the Power of Coding Instruction for Math LLMs