This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. It is, for example, better at deductive than inductive reasoning. ChatGPT suffers from hallucination problems like other LLMs and it generates more extrinsic hallucinations from its parametric memory as it does not have access to an external knowledge base. Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn "prompt engineering" fashion. We also release codebase for evaluation set extraction.

翻译：本文提出一个框架，利用公开数据集对ChatGPT等交互式大语言模型（LLM）进行定量评估。我们基于覆盖8种常见自然语言处理应用任务的23个数据集，对ChatGPT展开全面的技术评估，并根据这些数据集及一个新设计的多模态数据集，考察了ChatGPT的多任务、多语言和多模态能力。研究发现：ChatGPT在大多数任务上优于采用零样本学习的LLM，甚至在某些任务上超越了经过微调的模型；在处理非拉丁语系文本的理解能力上优于生成能力；通过中间代码生成步骤，它能够根据文本提示生成多模态内容。此外，逻辑推理、非文本推理和常识推理下的10类推理任务中，ChatGPT的平均准确率为63.41%，因此其推理能力不可靠——例如其演绎推理优于归纳推理。与其他LLM类似，ChatGPT存在幻觉问题，由于缺乏外部知识库访问，其参数记忆会产生更多外部幻觉。最后，ChatGPT的交互特性使人类能够以多轮“提示工程”方式与底层LLM协作以提升性能，例如在摘要任务上实现8%的ROUGE-1提升，在机器翻译任务上实现2%的ChrF++提升。我们还发布了用于评估集提取的代码库。