Linguistic ambiguity analysis in ChatGPT
This work addresses linguistic ambiguity for NLP practitioners, but it is incremental as it builds on existing analysis of modern models.
The paper tackles the challenge of linguistic ambiguity in NLP by analyzing ChatGPT's performance, revealing its strengths and weaknesses and providing strategies to optimize its use.
Linguistic ambiguity is and has always been one of the main challenges in Natural Language Processing (NLP) systems. Modern Transformer architectures like BERT, T5 or more recently InstructGPT have achieved some impressive improvements in many NLP fields, but there is still plenty of work to do. Motivated by the uproar caused by ChatGPT, in this paper we provide an introduction to linguistic ambiguity, its varieties and their relevance in modern NLP, and perform an extensive empiric analysis. ChatGPT strengths and weaknesses are revealed, as well as strategies to get the most of this model.