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.

该论文研究了自然语言生成中模型产生幻觉的问题，提出了序列级确定性作为幻觉的共同主题，并探索了模型响应中序列级确定性与幻觉水平之间的关系。通过在基于知识的对话生成任务上的实验证明，模型响应中更高水平的概率确定性和语义确定性与更低水平的幻觉显著相关。此外，论文还提出了基于确定性的响应排序方法以减少幻觉的产生。