Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain (2305.18324v1)

Abstract: A common way to use large pre-trained LLMs for downstream tasks is to fine tune them using additional layers. This may not work well if downstream domain is a specialized domain whereas the LLM has been pre-trained on a generic corpus. In this paper, we discuss the use of regular expression patterns employed as features for domain knowledge during the process of fine tuning, in addition to domain specific text. Our experiments on real scenario production data show that this method of fine tuning improves the downstream text classification tasks as compared to fine tuning only on domain specific text. We also show that the use of attention network for fine tuning improves results compared to simple linear layers.