Linguistic ambiguity analysis in ChatGPT
Miguel Ortega-Mart\'in, \'Oscar Garc\'ia-Sierra, Alfonso Ardoiz, Jorge, \'Alvarez, Juan Carlos Armenteros, Adri\'an Alonso
TL;DR
This paper analyzes linguistic ambiguity in NLP, focusing on ChatGPT's strengths and weaknesses, and offers strategies to optimize its performance based on an extensive empirical study.
Contribution
It provides a detailed empirical analysis of linguistic ambiguity in ChatGPT, highlighting its limitations and proposing strategies to improve its handling of ambiguous language.
Findings
ChatGPT exhibits specific weaknesses in resolving linguistic ambiguities.
Certain prompt strategies can enhance ChatGPT's disambiguation capabilities.
The analysis offers insights into optimizing NLP systems for ambiguity handling.
Abstract
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.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsMulti-Head Attention · Attention Is All You Need · Absolute Position Encodings · Gated Linear Unit · Adafactor · SentencePiece · Inverse Square Root Schedule · Position-Wise Feed-Forward Layer · Refunds@Expedia|||How do I get a full refund from Expedia? · WordPiece