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 claims, where good verification models do not always
exist. In this work, we introduce SciFix, a scientific claim correction system
that does not require a verifier but can outperform existing methods by a
considerable margin -- achieving correction accuracy of 84% on the SciFact
dataset, 77% on SciFact-Open and 72% on the CovidFact dataset, compared to next
best accuracies of 7%, 5%, and 15% on the same datasets 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 outperforms the very LLM that was
used to generate the annotated dataset -- with Few-Shot Prompting on GPT3.5
achieving 58%, 61%, and 64% on the respective datasets, a consistently lower
correction accuracy, despite using nearly 800 times as many parameters as our
model.Comment: To appear in proceedings of EMNLP2023 (findings