| Texto completo | |
| Autor(es): |
Nascimento Rocha, Victor Hugo
;
Silveira, Igor Cataneo
;
Pirozelli, Paulo
;
Maua, Denis Deratani
;
Cozman, Fabio Gagliardi
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2023, PT I; v. 14115, p. 13-pg., 2023-01-01. |
| Resumo | |
The recent success of Large Language Models (LLMs) has sparked concerns about their potential to spread misinformation. As a result, there is a pressing need for tools to identify "fake arguments" generated by such models. To create these tools, examples of texts generated by LLMs are needed. This paper introduces a methodology to obtain good, bad and ugly arguments from argumentative essays produced by ChatGPT, OpenAI's LLM. We then describe a novel dataset containing a set of diverse arguments, ArGPT. We assess the effectiveness of our dataset and establish baselines for several argumentation-related tasks. Finally, we show that the artificially generated data relates well to human argumentation and thus is useful as a tool to train and test systems for the defined tasks. (AU) | |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada |
| Processo FAPESP: | 22/02937-9 - Indução de programas lógico-probabilístico-neurais |
| Beneficiário: | Denis Deratani Mauá |
| Modalidade de apoio: | Auxílio à Pesquisa - Projeto Inicial |
| Processo FAPESP: | 19/26762-0 - Estruturas Lógicas em Argumentação |
| Beneficiário: | Paulo Pirozelli Almeida Silva |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |