  Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used
to train forward and reverse neural models that generate text from KG and vice
versa. However models trained on datasets where KG and text pairs are not
equivalent can suffer from more hallucination and poorer recall. In this paper,
we verify this empirically by generating datasets with different levels of
noise and find that noisier datasets do indeed lead to more hallucination. We
argue that the ability of forward and reverse models trained on a dataset to
cyclically regenerate source KG or text is a proxy for the equivalence between
the KG and the text in the dataset. Using cyclic evaluation we find that
manually created WebNLG is much better than automatically created TeKGen and
T-REx. Guided by these observations, we construct a new, improved dataset
called LAGRANGE using heuristics meant to improve equivalence between KG and
text and show the impact of each of the heuristics on cyclic evaluation. We
also construct two synthetic datasets using large language models (LLMs), and
observe that these are conducive to models that perform significantly well on
cyclic generation of text, but less so on cyclic generation of KGs, probably
because of a lack of a consistent underlying ontology.
