Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. However, there is a current hot debate regarding their reasoning capacity. In this paper, we examine the performance of GPT-3.5 and GPT-4 models, by performing a thorough technical evaluation on different reasoning tasks across eleven distinct datasets. Our findings show that GPT-4 outperforms GPT-3.5 in zero-shot learning throughout almost all evaluated tasks. In addition, we note that both models exhibit limited performance in Inductive, Mathematical, and Multi-hop Reasoning Tasks. While it may seem intuitive that the GPT-4 model would outperform GPT-3.5 given its size and efficiency in various NLP tasks, our paper offers empirical evidence to support this claim. We provide a detailed and comprehensive analysis of the results from both models to further support our findings. In addition, we propose a set of engineered prompts that improves performance of both models on zero-shot learning.

本文对GPT-3.5和GPT-4进行全面技术评估，发现GPT-4在几乎所有测试任务中优于GPT-3.5，并提出一组改良数据来提高两种模型的零样本学习能力。

GPT-3.5与GPT-4：在零样本学习中评估ChatGPT的推理性能