MARG: Multi-Agent Review Generation for Scientific Papers
Mike D'Arcy Tom Hope Larry Birnbaum Doug Downey
We study the ability of LLMs to generate feedback for scientific papers and
develop MARG, a feedback generation approach using multiple LLM instances that
engage in internal discussion. By distributing paper text across agents, MARG
can consume the full text of papers beyond the input length limitations of the
base LLM, and by specializing agents and incorporating sub-tasks tailored to
different comment types (experiments, clarity, impact) it improves the
helpfulness and specificity of feedback. In a user study, baseline methods
using GPT-4 were rated as producing generic or very generic comments more than
half the time, and only 1.7 comments per paper were rated as good overall in
the best baseline. Our system substantially improves the ability of GPT-4 to
generate specific and helpful feedback, reducing the rate of generic comments
from 60% to 29% and generating 3.7 good comments per paper (a 2.2x
improvement).