Solution generation for university math problems using large language models

Solution generation for university math problems using large language models

This study assesses the capabilities of cutting-edge Large Language Models (LLMs) including GPT-3.5 Turbo, GPT-4, and Gemini Pro in solving university-level math problems, with a focus on enhancing both accuracy and comprehensive explanation generation to aid in mathematical reasoning education....

Full description

Saved in:
Bibliographic Details
Main Author: Wirja, Louis
Other Authors: Owen Noel Newton Fernando
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Large language models
Generative AI
Deep learning
Transformers
Machine learning
Prompt engineering
Online Access:https://hdl.handle.net/10356/175331
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:This study assesses the capabilities of cutting-edge Large Language Models (LLMs) including GPT-3.5 Turbo, GPT-4, and Gemini Pro in solving university-level math problems, with a focus on enhancing both accuracy and comprehensive explanation generation to aid in mathematical reasoning education. Through rigorous evaluation on a curated problem set spanning university calculus topics, we explore zero-shot and few-shot learning scenarios, measuring performance via accuracy and semantic similarity. Additionally, an ensemble model combining GPT-3.5 Turbo and Gemini Pro shows improved efficacy compared to individual components. By combining precise solutions with clear, step-by-step explanations, our study aims to provide students with vital tools for learning complex concepts and developing mathematical intuition

Similar Items