A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
The development of large language models (LLMs) such as ChatGPT has brought a
lot of attention recently. However, their evaluation in the benchmark academic
datasets remains under-explored due to the difficulty of evaluating the
generative outputs produced by this model against the ground truth. In this
paper, we aim to present a thorough evaluation of ChatGPT's performance on
diverse academic datasets, covering tasks like question-answering, text
summarization, code generation, commonsense reasoning, mathematical
problem-solving, machine translation, bias detection, and ethical
considerations. Specifically, we evaluate ChatGPT across 140 tasks and analyze
255K responses it generates in these datasets. This makes our work the largest
evaluation of ChatGPT in NLP benchmarks. In short, our study aims to validate
the strengths and weaknesses of ChatGPT in various tasks and provide insights
for future research using LLMs. We also report a new emergent ability to follow
multi-query instructions that we mostly found in ChatGPT and other
instruction-tuned models. Our extensive evaluation shows that even though
ChatGPT is capable of performing a wide variety of tasks, and may obtain
impressive performance in several benchmark datasets, it is still far from
achieving the ability to reliably solve many challenging tasks. By providing a
thorough assessment of ChatGPT's performance across diverse NLP tasks, this
paper sets the stage for a targeted deployment of ChatGPT-like LLMs in
real-world applications.