NLI-based. Dušek and Kasner [39] recognize the textual entailment between the input data and
the output text for both omissions and hallucinations with an NLI model. This work measures the
semantic accuracy in two directions: check omissions by inferring whether the input fact is entailed
by the generated text and check hallucinations by inferring the generated text from the input.

ACM Comput. Surv., Vol. 1, No. 1, Article . Publication date: February 2022.

28

Ziwei Ji, et al.

LM-based. Filippova [50], Tian et al. [184] are based on the intuition that when an unconditional
LM, only trained on the targets, gets a smaller loss than a conditional 𝐿𝑀𝑥, trained on both sources
and targets, the token is predicted unfaithfully. Thus, they calculate the ratio of hallucinated tokens
to the total target length to measure the hallucination level.

10.3 Hallucination Mitigation in Data-to-Text Generation
SurveyofHallucinationinNatural Language Generation