NLG systems often produce inaccurate but fluent outputs, leading to hallucinations that challenge correctness.
The author presents the results of the SHROOM shared task focused on detecting hallucinations in natural language generation systems. The task aimed to address the challenge of fluent but inaccurate outputs that jeopardize the correctness of NLG applications.