Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo

arXiv:2404.04103v1 Announce Type: new
Abstract: Neural Table-to-Text models tend to hallucinate, producing texts that contain factual errors. We investigate whether such errors in the output can be traced back to problems with the input. We manually annotated 1,837 texts generated by multiple models in the politics domain of the ToTTo dataset. We identify the input problems that are responsible for many output errors and show that fixing these inputs reduces factual errors by between 52% and 76% (depending on the …

abstract accuracy arxiv cs.cl domain errors generated improving multiple politics table text type