  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.
