Certificate of Completion
THIS ACKNOWLEDGES THAT
HAS COMPLETED THE MAY-SUMMER 2024 DATA SCIENCE BOOT CAMP
Matthew Gelvin
Roman Holowinsky, PhD
JUNE 10, 2024
DIRECTOR
DATE
TEAM
QED
Cisil Karaguzel, Ming Zhang, Hatice Mutlu, Adnan Cihan Cakar, Matthew Gelvin
The state-of-the-art language models have achieved human-level performance on many tasks but still face significant challenges in multi-step mathematical reasoning. Recent advancements in large language models (LLMs) have demonstrated exceptional capabilities across diverse tasks, including common-sense reasoning, question answering, and summarization. However, they struggle with tasks requiring quantitative reasoning, such as solving complex mathematical problems. Mathematics serves as a valuable testbed in machine learning for problem-solving abilities, highlighting the need for more robust models capable of multi-step reasoning. The primary goal of this project is to develop a customized LLM that can provide step-by-step solutions to math problems by fine-tuning a base LLM using a large mathematical dataset.