Preste atenção
a veloz evolução do processamento de linguagem natural e onde ela empaca
DOI:
https://doi.org/10.35699/2965-6931.2023.47510Palavras-chave:
modelos de linguagem, aprendizado profundo, processamento de linguagem natural, inteligência artificialResumo
Este artigo analisa a evolução dos modelos baseados em atenção no Processamento de Linguagem Natural (PLN) em um tom informal, começando em 2003 e culminando nas arquiteturas de “transformers" que conhecemos desde 2017. Explicamos como os “transformers" conseguiram resolver o importante "benchmark" para o raciocínio de senso comum em Inteligência Artificial devido ao seu pré-treinamento. Além disso, investigamos o paralelo entre o conceito de ‘gist' (“o que realmente importa”) na compreensão da linguagem humana, conforme proposto por Roger Schank, um veterano do PLN, e os “embeddings" agora empregados na aprendizagem de máquina. No final do artigo, discutimos um problema bem conhecido com esses modelos, as chamadas "alucinações". Este fenômeno destaca a luta dos modelos para discernir fato de ficção, necessitando de mais pesquisas para mitigar seu impacto. Enquadramos essa questão no contexto do trabalho de David Lewis, argumentando que representa um desafio fundamental para os modelos de linguagem.
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