  One of the most important yet onerous tasks in the academic peer-reviewing
process is composing meta-reviews, which involves understanding the core
contributions, strengths, and weaknesses of a scholarly manuscript based on
peer-review narratives from multiple experts and then summarizing those
multiple experts' perspectives into a concise holistic overview. Given the
latest major developments in generative AI, especially Large Language Models
(LLMs), it is very compelling to rigorously study the utility of LLMs in
generating such meta-reviews in an academic peer-review setting. In this paper,
we perform a case study with three popular LLMs, i.e., GPT-3.5, LLaMA2, and
PaLM2, to automatically generate meta-reviews by prompting them with different
types/levels of prompts based on the recently proposed TELeR taxonomy. Finally,
we perform a detailed qualitative study of the meta-reviews generated by the
LLMs and summarize our findings and recommendations for prompting LLMs for this
complex task.
