We propose Iterative Facuality Refining on Informative Scientific
Question-Answering (ISQA) feedback\footnote{Code is available at
https://github.com/lizekai-richard/isqa}, a method following human
learning theories that employs model-generated feedback consisting of both
positive and negative information. Through iterative refining of summaries, it
probes for the underlying rationale of statements to enhance the factuality of
scientific summarization. ISQA does this in a fine-grained manner by asking a
summarization agent to reinforce validated statements in positive feedback and
fix incorrect ones in negative feedback. Our findings demonstrate that the ISQA
feedback mechanism significantly improves the factuality of various open-source
LLMs on the summarization task, as evaluated across multiple scientific
datasets.

我们提出了迭代性的逐渐精化的信息科学问答（ISQA）反馈方法，该方法遵循人类学习理论，利用模型生成的正负反馈来增强科学概述的真实性，通过迭代细化概述探索语句的基本原理。这种 ISQA 以细粒度的方式进行，通过要求概述代理在正反馈中加强验证的语句，并在负反馈中修复不正确的语句。我们的研究结果表明，ISQA 反馈机制显著提高了多个科学数据集上针对概述任务的开源 LLMs 的真实性。