Pre-trained Language Models for Multi-Label Text Classification of Competitive Programming Problems - Souza, Bruno Vargas de; Silvestre, Ana Sofia S.; Lisboa, Victor Hugo F.; Borges, Vinicius R. P. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e Computacional, 2024)
This paper explores the use of pre-trained language models for classifying programming problems from online judges based on topics commonly addressed in competitive programming. State-of-the-art language models were employed as text classifiers, including Long Short-Term Memory (LSTM) and Bidirectional LSTM with pre-trained Word2Vec embeddings, Bidirectional Encoder Representations from Transformers (BERT), and Llama3.1-8B. Experiments were conducted using two different representations of the programming problems: standalone statement and statement with source code. The results showed that Llama3.1-8B achieved the best overall Macro F1-Score, outperforming the other models by a significant margin.