Towards a Framework for Hierarchical Text Segmentation using Large Language Models

Thanks to the in context learning abilities of LLMs, building a text classifier without access to labeled data is easier than ever. However, for more complex tasks than simple classification, the difficulties remain. One such task is hierarchical segmentation where a model needs to break down an input document in an hierarchy of segments each annotated with a label from a taxonomy of classes. The long input documents, the large input class taxonomies as well as the increased number of outputs make the problem challenging to solve with a single LLM call. While hierarchical segmentation is amenable to splitting in smaller more manageable tasks, there is a huge design space of such approaches making the process tedious and time consuming. To reduce the amount of ad hoc exploration and implementation, we propose the first framework for hierarchical text segmentation using LLMs. The key idea behind our framework is that hierarchical segmentation can be viewed as a join between document and taxonomy. Inspired by join operator design, we propose two highly configurable hierarchical segmentation algorithms based on index and merge sort joins. Our experiments highlight the existence of trade offs across algorithms and their configurations, indicating that machine learning engineers may benefit from quickly exploring the design space using our framework.