In this paper, we present our novel systems developed for the SemEval-2024
hallucination detection task. Our investigation spans a range of strategies to
compare model predictions with reference standards, encompassing diverse
baselines, the refinement of pre-trained encoders through supervised learning,
and an ensemble approaches utilizing several high-performing models. Through
these explorations, we introduce three distinct methods that exhibit strong
performance metrics. To amplify our training data, we generate additional
training samples from unlabelled training subset. Furthermore, we provide a
detailed comparative analysis of our approaches. Notably, our premier method
achieved a commendable 9th place in the competition's model-agnostic track and
17th place in model-aware track, highlighting its effectiveness and potential.

我们在 SemEval-2024 妄想检测任务中开发了新的系统，通过比较模型预测与参考标准，使用多种基线、通过监督学习改进预训练编码器以及使用几个高性能模型的集成方法，我们介绍了三种不同的方法，表现出强大的性能指标，并通过生成额外的训练样本来增加训练数据，此外，还提供了详细的比较分析，值得注意的是，我们的首要方法在比赛的模型无关轨道中获得了可称赞的第 9 名，模型感知轨道中获得了第 17 名，突显了其有效性和潜力。