CITATION.cff

cff-version: 1.2.0
message: "If you use this software, please cite the paper as below."
date-released: 2023-10-04
preferred-citation:
  type: conference-paper
  title: "Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference"
  abstract: "For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a school's curriculum. One potential solution is retrieval-augmented generation (RAG), which involves incorporating a vetted external knowledge source in the LLM prompt to increase response quality. In this paper, we designed prompts that retrieve and use content from a high-quality open-source math textbook to generate responses to real student questions. We evaluate the efficacy of this RAG system for middle-school algebra and geometry QA by administering a multi-condition survey, finding that humans prefer responses generated using RAG, but not when responses are too grounded in the textbook content. We argue that while RAG is able to improve response quality, designers of math QA systems must consider trade-offs between generating responses preferred by students and responses closely matched to specific educational resources."
  doi: 10.48550/arXiv.2310.03184
  year: 2023
  conference:
      name: "NeurIPS'23 Workshop on Generative AI for Education (GAIED)"
      city: "New Orleans"
      country: "US"
      date-start: "2023-12-15"
      date-end: "2023-12-15"
  authors:
  - family-names: Levonian
    given-names: Zachary
    orcid: https://orcid.org/0000-0002-8932-1489
  - family-names: Li
    given-names: Chenglu
  - family-names: Zhu
    given-names: Wangda
  - family-names: Gade
    given-names: Anoushka
  - family-names: Henkel
    given-names: Owen
  - family-names: Postle
    given-names: Millie-Ellen
  - family-names: Xing
    given-names: Wanli
authors:
  - family-names: Levonian
    given-names: Zachary
    orcid: https://orcid.org/0000-0002-8932-1489
  - family-names: Henkel
    given-names: Owen
  - family-names: Roberts
    given-names: Bill
title: "llm-math-education: Retrieval augmented generation for middle-school math question answering and hint generation"
abstract: "How can we incorporate trusted, external math knowledge in generated answers to student questions? llm-math-education is a Python package that implements basic retrieval augmented generation (RAG) and contains prompts for two primary use cases: general math question-answering (QA) and hint generation."
version: 0.5.1
doi: 10.5281/zenodo.8284412
date-released: 2023-08-25
license: MIT
repository-code: "https://github.com/levon003/llm-math-education"