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).

通过使用多个 LLM 实例进行内部讨论，我们研究了 LLM 生成科学论文反馈的能力，并开发了一种名为 MARG 的反馈生成方法。在用户研究中，我们的系统显著提高了 GPT-4 生成具体和有帮助反馈的能力，将生成的普通评论的比例从 60％降低到 29％，并每篇论文生成 3.7 条好的评论（提升了 2.2 倍）。