Due to the prohibitively high cost of creating error correction datasets,
most Factual Claim Correction methods rely on a powerful verification model to
guide the correction process. This leads to a significant drop in performance
in domains like Scientific Claim Correction, where good verification models do
not always exist. In this work, we introduce a claim correction system that
makes no domain assumptions and does not require a verifier but is able to
outperform existing methods by an order of magnitude -- achieving 94%
correction accuracy on the SciFact dataset, and 62.5% on the SciFact-Open
dataset, compared to the next best methods 0.5% and 1.50% respectively. Our
method leverages the power of prompting with LLMs during training to create a
richly annotated dataset that can be used for fully supervised training and
regularization. We additionally use a claim-aware decoding procedure to improve
the quality of corrected claims. Our method is competitive with the very LLM
that was used to generate the annotated dataset -- with GPT3.5 achieving 89.5%
and 60% correction accuracy on SciFact and SciFact-Open, despite using 1250
times as many parameters as our model.

本文介绍了一个声明校正系统，该系统不需要验证器，并采用了 LLMs 提示和声明感知解码程序进行培训来创建可用于完全监督培训和正则化的丰富注释数据集，在科学事实数据集上可实现 94％纠正准确率。