Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance
Lefteris Loukas Ilias Stogiannidis Prodromos Malakasiotis Stavros Vassos
We propose the use of conversational GPT models for easy and quick few-shot
text classification in the financial domain using the Banking77 dataset. Our
approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes
the technical expertise required and eliminates the need for expensive GPU
computing while yielding quick and accurate results. Additionally, we fine-tune
other pre-trained, masked language models with SetFit, a recent contrastive
learning technique, to achieve state-of-the-art results both in full-data and
few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can
outperform fine-tuned, non-generative models even with fewer examples. However,
subscription fees associated with these solutions may be considered costly for
small organizations. Lastly, we find that generative models perform better on
the given task when shown representative samples selected by a human expert
rather than when shown random ones. We conclude that a) our proposed methods
offer a practical solution for few-shot tasks in datasets with limited label
availability, and b) our state-of-the-art results can inspire future work in
the area.