Empirical Study of LLM Fine-Tuning for Text Classification in Legal Document Review

The increased integration of Large Language Models (LLMs) across industry sectors is enabling domain experts with new text classification optimization methods. These LLMs are pretrained on exceedingly large amounts of data; however, practitioners can perform additional training, or "fine-tuning," to improve their text classifier's results for their own use cases. This paper presents a series of experiments comparing a standard, pretrained DistilBERT model and a fine-tuned DistilBERT model, both leveraged for the downstream NLP task of text classification. Tuning the model using domain-specific data from real-world legal matters suggests fine-tuning improves the performance of LLM text classifiers.To evaluate the performance of text classification models, using these two Large Language Models, we employed two distinct approaches that 1) score a whole document's text for prediction and 2) score snippets (sentence-level components of a document) of text for prediction. When comparing the two approaches we found that one prediction method outperforms the other, depending on the use case.