2023/12/22 by Yuhao Chen, Chloe Wong, Chen, Yuhao +22
Psychology · Social Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Digital Mental Health Interventions #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mental Health Research Topics
paper · pdf · doi:10.48550/arxiv.2312.15006
openalex publication_date 2023/12/22 · openalex created_date 2023/12/29 · openalex updated_date 2026/07/28
This study critically evaluates the efficacy of prompting methods in enhancing the mathematical reasoning capability of large language models (LLMs). The investigation uses three prescriptive prompting methods - simple, persona, and conversational prompting - known for their effectiveness in enhancing the linguistic tasks of LLMs. We conduct this analysis on OpenAI's LLM chatbot, ChatGPT-3.5, on extensive problem sets from the MATH, GSM8K, and MMLU datasets, encompassing a broad spectrum of mathematical challenges. A grading script adapted to each dataset is used to determine the effectiveness of these prompting interventions in enhancing the model's mathematical analysis power. Contrary to expectations, our empirical analysis reveals that none of the investigated methods consistently improves over ChatGPT-3.5's baseline performance, with some causing significant degradation. Our findings suggest that prompting strategies do not necessarily generalize to new domains, in this study failing to enhance mathematical performance.