SBI-RAG：透過基於模式的教學和檢索增強生成來提升學生的數學應用問題解決能力

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

October 17, 2024

作者: Prakhar Dixit, Tim Oates

cs.AI

摘要

許多學生在數學應用題中遇到困難，常常難以識別關鍵信息並選擇適當的數學運算。基於模式的教學（SBI）是一種證據支持的策略，幫助學生根據問題結構進行分類，提高解題準確性。在此基礎上，我們提出了一個基於模式的教學檢索增強生成（SBI-RAG）框架，融入了大型語言模型（LLM）。我們的方法強調逐步推理，通過利用模式來引導解決方案生成。我們在GSM8K數據集上評估其性能，並與GPT-4和GPT-3.5 Turbo進行比較，引入了“推理分數”指標來評估解決方案的質量。我們的研究結果表明，SBI-RAG提升了推理清晰度和解題準確性，可能為學生提供教育上的好處。

English

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 problem-solving accuracy, potentially providing educational benefits for students