  Fact-checking based on commercial LLMs has become mainstream. Although these
methods offer high explainability, it falls short in accuracy compared to
traditional fine-tuning approaches, and data security is also a significant
concern. In this paper, we propose a self-instruction based fine-tuning
approach for fact-checking that balances accuracy and explainability. Our
method consists of Data Augmentation and Improved DPO fine-tuning. The former
starts by instructing the model to generate both positive and negative
explanations based on claim-evidence pairs and labels, then sampling the
dataset according to our customized difficulty standards. The latter employs
our proposed improved DPO to fine-tune the model using the generated samples.
We fine-tune the smallest-scale LLaMA-7B model and evaluate it on the
challenging fact-checking datasets FEVEROUS and HOVER, utilizing four
fine-tuning methods and three few-shot learning methods for comparison. The
experiments demonstrate that our approach not only retains accuracy comparable
to, or even surpassing, traditional fine-tuning methods, but also generates
fluent explanation text. Moreover, it also exhibit high generalization
performance. Our method is the first to leverage self-supervised learning for
fact-checking and innovatively combines contrastive learning and improved DPO
in fine-tuning LLMs, as shown in the experiments.
