Summary of Sbi-rag: Enhancing Math Word Problem Solving For Students Through Schema-based Instruction and Retrieval-augmented Generation, by Prakhar Dixit et al.
SBI-RAG: Enhancing Math Word Problem Solving for Students through Schema-Based Instruction and Retrieval-Augmented Generation
by Prakhar Dixit, Tim Oates
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Schema-Based Instruction Retrieval-Augmented Generation (SBI-RAG) framework integrates a large language model (LLM) to improve math word problem-solving accuracy. By leveraging schemas, the approach emphasizes step-by-step reasoning and guides solution generation. The SBI-RAG framework is evaluated on the GSM8K dataset, comparing its performance with GPT-4 and GPT-3.5 Turbo, and introduces a “reasoning score” metric to assess solution quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps students solve math word problems by teaching them to identify key information and choose the right mathematical operations. The researchers created a new way to help called Schema-Based Instruction Retrieval-Augmented Generation (SBI-RAG). This approach uses big language models to guide students in solving problems step-by-step. The team tested SBI-RAG on many math word problem examples, comparing it to other approaches like GPT-4 and GPT-3.5 Turbo. They also created a new way to measure how well the solutions are done. |
Keywords
» Artificial intelligence » Gpt » Large language model » Rag » Retrieval augmented generation
