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。

大规模语言模型（LLMs）中的语义歧义分析