Linguistic ambiguity continues to represent a significant challenge for
natural language processing (NLP) systems, notwithstanding the advancements in
architectures such as Transformers and BERT. Inspired by the recent success of
instructional models like ChatGPT and Gemini (In 2023, the artificial
intelligence was called Bard.), this study aims to analyze and discuss
linguistic ambiguity within these models, focusing on three types prevalent in
Brazilian Portuguese: semantic, syntactic, and lexical ambiguity. We create a
corpus comprising 120 sentences, both ambiguous and unambiguous, for
classification, explanation, and disambiguation. The models capability to
generate ambiguous sentences was also explored by soliciting sets of sentences
for each type of ambiguity. The results underwent qualitative analysis, drawing
on recognized linguistic references, and quantitative assessment based on the
accuracy of the responses obtained. It was evidenced that even the most
sophisticated models, such as ChatGPT and Gemini, exhibit errors and
deficiencies in their responses, with explanations often providing
inconsistent. Furthermore, the accuracy peaked at 49.58 percent, indicating the
need for descriptive studies for supervised learning.

本研究旨在分析和讨论 ChatGPT 和 Gemini 等模型中的语言歧义，关注巴西葡萄牙语中的语义、句法和词汇歧义。通过创建一个包含 120 个歧义和非歧义句子的语料库，进行分类、解释和消除歧义。结果经过定性分析和基于回答准确性的定量评估，证明了即使是像 ChatGPT 和 Gemini 这样先进的模型在回答中也存在错误和不足，解释通常是不一致的。此外，准确性达到了 49.58％的峰值，表明有必要进行有监督学习的描述性研究。