Conditional language models still generate unfaithful output that is not supported by their input. These unfaithful generations jeopardize trust in real-world applications such as summarization or human-machine interaction, motivating a need for automatic faithfulness metrics. To implement such metrics, NLI models seem attractive, since they solve a strongly related task that comes with a wealth of prior research and data. But recent research suggests that NLI models require costly additional machinery to perform reliably across datasets, e.g., by running inference on a cartesian product of input and generated sentences, or supporting them with a question-generation/answering step. In this work we show that pure NLI models _can_ outperform more complex metrics when combining task-adaptive data augmentation with robust inference procedures. We propose: (1) Augmenting NLI training data to adapt NL inferences to the specificities of faithfulness prediction in dialogue; (2) Making use of both entailment and contradiction probabilities in NLI, and (3) Using Monte-Carlo dropout during inference. Applied to the TRUE benchmark, which combines faithfulness datasets across diverse domains and tasks, our approach strongly improves a vanilla NLI model and significantly outperforms previous work, while showing favourable computational cost.

翻译：条件语言模型仍会生成不忠实于其输入的输出，这损害了其在摘要生成或人机交互等实际应用中的可信度，因此亟需自动化的忠实度评估指标。自然语言推理（NLI）模型因其与忠实度预测高度相关的任务特性，且具备丰富的既有研究与数据资源，似乎颇具应用前景。然而近期研究表明，NLI模型需依赖昂贵的附加机制（如对输入与生成句的笛卡尔积进行推理推断，或辅以问题生成/回答流程）方能跨数据集可靠运行。本研究发现，将任务自适应数据增强与鲁棒推断流程相结合，纯NLI模型可超越更复杂的指标。我们提出：（1）通过扩充NLI训练数据，使自然语言推理适应对话场景中忠实度预测的特殊性；（2）同时利用NLI中的蕴含概率与矛盾概率；（3）在推断阶段采用蒙特卡洛丢弃法。在融合多领域多任务忠实度数据集的TRUE基准测试中，我们的方法显著提升了原始NLI模型性能，超越此前最优成果，同时展现出更优的计算成本。