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.

翻译：ChatGPT等大语言模型（LLMs）的开发近期引发了广泛关注。然而，由于针对该模型生成的输出结果与真实标注之间的评估存在困难，其在基准学术数据集上的表现仍缺乏充分探索。本文旨在对ChatGPT在多样化学术数据集上的性能进行全面评估，涵盖问答、文本摘要、代码生成、常识推理、数学问题求解、机器翻译、偏见检测及伦理考量等任务。具体而言，我们评估了ChatGPT在140项任务中的表现，分析了其生成的25.5万条响应。这使得本研究成为NLP基准测试中对ChatGPT规模最大的一次评估。简言之，本研究旨在验证ChatGPT在不同任务中的优势与不足，并为未来使用LLM的研究提供见解。我们还发现了ChatGPT及一系列指令微调模型所具备的新型涌现能力——遵循多查询指令。广泛的评估表明，尽管ChatGPT能执行多种任务，并在若干基准数据集上取得令人瞩目的表现，但其在可靠解决诸多复杂任务方面仍有显著差距。通过系统评估ChatGPT在多样化NLP任务中的表现，本文为ChatGPT类LLM在现实应用中的精准部署奠定了基础。