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Certificate of Completion

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THIS ACKNOWLEDGES THAT

HAS COMPLETED THE MAY-SUMMER 2024 DATA SCIENCE BOOT CAMP

Matthew Gelvin

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Roman Holowinsky, PhD

JUNE 10, 2024

DIRECTOR

DATE

TEAM

QED

Cisil Karaguzel, Ming Zhang, Hatice Mutlu, Adnan Cihan Cakar, Matthew Gelvin

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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.

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