  Recent evaluations of Large Language Models (LLMs) have centered around
testing their zero-shot/few-shot capabilities for basic natural language tasks
and their ability to translate instructions into tool APIs. However, the
evaluation of LLMs utilizing complex tools to finish multi-turn, multi-modal
instructions in a complex multi-modal environment has not been investigated. To
address this gap, we introduce the PowerPoint Task Completion (PPTC) benchmark
to assess LLMs' ability to create and edit PPT files based on user
instructions. It contains 279 multi-turn sessions covering diverse topics and
hundreds of instructions involving multi-modal operations. We also propose the
PPTX-Match Evaluation System that evaluates if LLMs finish the instruction
based on the prediction file rather than the label API sequence, thus it
supports various LLM-generated API sequences. We measure 3 closed LLMs and 6
open-source LLMs. The results show that GPT-4 outperforms other LLMs with
75.1\% accuracy in single-turn dialogue testing but faces challenges in
completing entire sessions, achieving just 6\% session accuracy. We find three
main error causes in our benchmark: error accumulation in the multi-turn
session, long PPT template processing, and multi-modality perception. These
pose great challenges for future LLM and agent systems. We release the data,
code, and evaluation system of PPTC at \url{https://github.com/gydpku/PPTC}.
