Model hallucination has been a crucial interest of research in Natural Language Generation (NLG). In this work, we propose sequence-level certainty as a common theme over hallucination in NLG, and explore the correlation between sequence-level certainty and the level of hallucination in model responses. We categorize sequence-level certainty into two aspects: probabilistic certainty and semantic certainty, and reveal through experiments on Knowledge-Grounded Dialogue Generation (KGDG) task that both a higher level of probabilistic certainty and a higher level of semantic certainty in model responses are significantly correlated with a lower level of hallucination. What's more, we provide theoretical proof and analysis to show that semantic certainty is a good estimator of probabilistic certainty, and therefore has the potential as an alternative to probability-based certainty estimation in black-box scenarios. Based on the observation on the relationship between certainty and hallucination, we further propose Certainty-based Response Ranking (CRR), a decoding-time method for mitigating hallucination in NLG. Based on our categorization of sequence-level certainty, we propose 2 types of CRR approach: Probabilistic CRR (P-CRR) and Semantic CRR (S-CRR). P-CRR ranks individually sampled model responses using their arithmetic mean log-probability of the entire sequence. S-CRR approaches certainty estimation from meaning-space, and ranks a number of model response candidates based on their semantic certainty level, which is estimated by the entailment-based Agreement Score (AS). Through extensive experiments across 3 KGDG datasets, 3 decoding methods, and on 4 different models, we validate the effectiveness of our 2 proposed CRR methods to reduce model hallucination.

翻译：模型幻觉一直是自然语言生成（NLG）研究的关键关注点。本文提出序列级确定性作为NLG中幻觉现象的共性主题，并探究序列级确定性与模型响应中幻觉程度之间的关联。我们将序列级确定性分为两个层面：概率确定性和语义确定性，通过在知识引导对话生成（KGDG）任务上的实验揭示，模型响应中较高的概率确定性水平和较高的语义确定性水平均与较低的幻觉程度显著相关。此外，我们提供了理论证明与分析，表明语义确定性是概率确定性的良好估计量，因此具备在黑盒场景中替代基于概率的确定性估计的潜力。基于对确定性与幻觉之间关系的观察，我们进一步提出基于确定性的响应排序（CRR）——一种用于缓解NLG中幻觉的解码时方法。根据序列级确定性的分类，我们提出两类CRR方法：概率CRR（P-CRR）和语义CRR（S-CRR）。P-CRR通过计算整个序列的算术平均对数概率对独立采样的模型响应进行排序。S-CRR从意义空间进行确定性估计，并基于由蕴含一致性分数（AS）评估的语义确定性水平对多个候选模型响应进行排序。通过在3个KGDG数据集、3种解码方法和4种不同模型上的广泛实验，我们验证了所提出的两种CRR方法在减少模型幻觉方面的有效性。