Experimental Study of In-Context Learning for Text Classification and Its Application to Legal Document Review in Construction Delay Disputes

Text classification is a well-established area of machine learning that involves automatically categorizing text into predefined categories, such as positive or negative in sentiment analysis. It typically involves applying a machine learning algorithm to learn a predictive model from a set of labeled training texts and using the model to classify new texts. Large language models (LLMs) have been successfully applied to various natural language processing tasks, including text classification. There are two main approaches to text classification with LLMs: In-Context Learning and Fine-Tuning. In-Context Learning involves prompt engineering, allowing the model to learn a new task using only a few demonstration examples, while Fine-Tuning adjusts the model’s parameters with additional labeled data. Retrieval-Augmented Generation (RAG) is a retrieval process that enhances LLM performance by selecting relevant examples. This paper presents our work on utilizing In-Context Learning and RAG to identify delay-related statements during the document review process of construction delay disputes. We also report the results of our experiments comparing the accuracy of In-Context Learning with that of traditional machine learning algorithms, such as logistic regression and KNN.