Contextual stance classification using prompt engineering

Resumo


This paper introduces a prompt-based method for few-shot learning addressing, as an application example, contextual stance classification, that is, the task of determining the attitude expressed by a given statement within a conversation thread with multiple points of view towards another statement. More specifically, we envisaged a method that uses the existing conversation thread (i.e., messages that are part of the test data) to create natural language prompts for few-shot learning with minimal reliance on training samples, whose preliminary results suggest that prompt engineering may be a competitive alternative to supervised methods both in terms of accuracy and development costs for the task at hand.

Palavras-chave: stance classification, ChatGPT, prompt engineering

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Publicado
25/09/2023
DE FONSECA, Felipe Penhorate Carvalho; PARABONI, Ivandré; DIGIAMPIETRI, Luciano Antonio. Contextual stance classification using prompt engineering. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 14. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 33-42. DOI: https://doi.org/10.5753/stil.2023.233708.