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：大型语言模型（如ChatGPT）的近期发展引发了广泛关注。然而，由于难以将该模型生成的输出与真实标签进行对比评估，其在学术基准数据集上的表现仍未得到充分探索。本文旨在全面评估ChatGPT在不同学术数据集上的性能，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，我们在140项任务中评估了ChatGPT，并分析了其生成的255K条响应，这使其成为自然语言处理（NLP）基准测试中对ChatGPT规模最大的评估研究。简言之，本研究旨在验证ChatGPT在各任务中的优势与不足，并为未来基于大型语言模型的研究提供启示。我们还报告了一项新涌现的能力——遵循多查询指令，该能力主要存在于ChatGPT及其他经过指令微调的模型中。通过广泛评估发现：尽管ChatGPT能执行多种任务，并在部分基准数据集中取得令人瞩目的表现，但其距离可靠解决众多挑战性任务仍相去甚远。本文通过对ChatGPT在多样化NLP任务中的性能进行系统评估，为类ChatGPT大型语言模型在实际应用中的定向部署奠定了基础。