Large language models for aspect-based sentiment analysis
Paul F. Simmering Paavo Huoviala
Large language models (LLMs) offer unprecedented text completion
capabilities. As general models, they can fulfill a wide range of roles,
including those of more specialized models. We assess the performance of GPT-4
and GPT-3.5 in zero shot, few shot and fine-tuned settings on the aspect-based
sentiment analysis (ABSA) task. Fine-tuned GPT-3.5 achieves a state-of-the-art
F1 score of 83.8 on the joint aspect term extraction and polarity
classification task of the SemEval-2014 Task 4, improving upon InstructABSA
[@scaria_instructabsa_2023] by 5.7%. However, this comes at the price of 1000
times more model parameters and thus increased inference cost. We discuss the
the cost-performance trade-offs of different models, and analyze the typical
errors that they make. Our results also indicate that detailed prompts improve
performance in zero-shot and few-shot settings but are not necessary for
fine-tuned models. This evidence is relevant for practioners that are faced
with the choice of prompt engineering versus fine-tuning when using LLMs for
ABSA.