  Employing language models to generate explanations for an incoming implicit
hate post is an active area of research. The explanation is intended to make
explicit the underlying stereotype and aid content moderators. The training
often combines top-k relevant knowledge graph (KG) tuples to provide world
knowledge and improve performance on standard metrics. Interestingly, our study
presents conflicting evidence for the role of the quality of KG tuples in
generating implicit explanations. Consequently, simpler models incorporating
external toxicity signals outperform KG-infused models. Compared to the
KG-based setup, we observe a comparable performance for SBIC (LatentHatred)
datasets with a performance variation of +0.44 (+0.49), +1.83 (-1.56), and
-4.59 (+0.77) in BLEU, ROUGE-L, and BERTScore. Further human evaluation and
error analysis reveal that our proposed setup produces more precise
explanations than zero-shot GPT-3.5, highlighting the intricate nature of the
task.
