Authors:
Rajarshi Biswas
;
Sourav Dutta
and
Dirk Werth
Affiliation:
August-Wilhelm Scheer Institute, Uni-Campus D 5 1, 66123 Saarbrücken, Germany
Keyword(s):
Natural Language Processing, Natural Language Generation, Generative AI.
Abstract:
Generative artificial intelligence, in recent times, is producing tremendous interest across industry and academia leading to rapid growth. Developments in model architecture, training datasets and large scale computing enable the realization of impressive generative tasks in textual computing, computer vision etc. However, the generative processes suffer from various challenging artifacts that can generate confusion, risks or compromise the security. In this paper, we explore in detail the problem of inconsistent or hallucinogenic generation in natural language generation (NLG). We define the problem and survey the current techniques for detection, measurement and mitigation on five different tasks, which are, abstractive summarization, question answering, dialogue generation, machine translation and named entity recognition combined with information retrieval.