Neural knowledge-to-text generation models often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the given facts, or describe facts not present in the input. To reduce hallucinations, we propose a novel decoding method, TWEAK (Think While Effectively Articulating Knowledge). TWEAK treats the generated sequences at each decoding step and its future sequences as hypotheses, and ranks each generation candidate based on how well their corresponding hypotheses support the input facts using a Hypothesis Verification Model (HVM). We first demonstrate the effectiveness of TWEAK by using a Natural Language Inference (NLI) model as the HVM and report improved faithfulness with minimal impact on the quality. We then replace the NLI model with our task-specific HVM trained with a first-of-a-kind dataset, FATE (Fact-Aligned Textual Entailment), which pairs input facts with their faithful and hallucinated descriptions with the hallucinated spans marked. The new HVM improves the faithfulness and the quality further and runs faster. Overall the best TWEAK variants improve on average 2.22/7.17 points on faithfulness measured by FactKB over WebNLG and TekGen/GenWiki, respectively, with only 0.14/0.32 points degradation on quality measured by BERTScore over the same datasets. Since TWEAK is a decoding-only approach, it can be integrated with any neural generative model without retraining.

翻译：神经知识到文本生成模型往往难以忠实地生成输入事实的描述：它们可能产生与给定事实矛盾的幻觉，或描述输入中不存在的事实。为减少幻觉，我们提出了一种新颖的解码方法TWEAK（Think While Effectively Articulating Knowledge）。TWEAK将每个解码步生成的序列及其未来序列视为假设，并使用假设验证模型（HVM）根据这些假设对输入事实的支持程度对每个生成候选进行排序。我们首先通过使用自然语言推理（NLI）模型作为HVM验证了TWEAK的有效性，报告了在最小化质量影响下忠实度的提升。随后，我们将NLI模型替换为在首个此类数据集FATE（事实对齐文本蕴含）上训练的任务特定HVM，该数据集将输入事实与其忠实及带标记的幻觉描述配对。新的HVM进一步提升了忠实度和质量，且运行速度更快。总体而言，最佳TWEAK变体在WebNLG和TekGen/GenWiki数据集上，将FactKB测量的忠实度平均提高了2.22/7.17分，而同一数据集上BERTScore测量的质量仅下降了0.14/0.32分。由于TWEAK是一种仅解码方法，它可以与任何神经生成模型集成而无需重新训练。