Year: 2025
Type: conference-paper
Abstract: This study evaluates the effectiveness of three prompting strategies: standard prompting, chain-of-thought (CoT) prompting, and informed CoT prompting on the performance of the GPT-J model in solving mathematical reasoning tasks from the GSM8K dataset.Using the full test set of 1,319 problems, we assess the model's performance through accuracy, F1 score, BLEU, and ROUGE metrics.The findings suggest that while providing relevant context, such as math topics, can modestly enhance performance, the gains are limited.This underscores the importance of carefully designing prompts in adaptive systems and indicates that additional strategies may be necessary to achieve practical utility in educational applications.
Authors: Lev Sukherman, Yetunde Folajimi
Institution: Wentworth Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4411236282