incorporate a word alignment task into the translation model to improve the alignment accuracy
between the input and output, and thus faithfulness. Li et al. [103] combine an entailment task with
abstractive summarization to encourage models to generate summaries entailed by and faithful
to the source. Li et al. [102] incorporate rationale extraction and the answer generation, which
allows more confident and correct answers and reduces the hallucination problem. The Multi-task
approach has several advantages, such as data efficiency improvement, overfitting reduction, and
fast learning. It is crucial to choose which tasks should be learned jointly, and learning multiple
tasks simultaneously presents new challenges of design and optimization [25].
SurveyofHallucinationinNatural Language Generation