Concept Navigation and Classification via Open-Source Large Language Model Processing
It addresses the need for systematic analysis of complex discourses in fields like political science and media studies, though it appears incremental by combining existing methods.
The paper tackles the problem of detecting and classifying latent constructs like frames and topics from text using open-source LLMs, achieving enhanced accuracy and interpretability through a hybrid automated-human approach applied to diverse datasets such as AI policy debates and news articles.
This paper presents a novel methodological framework for detecting and classifying latent constructs, including frames, narratives, and topics, from textual data using Open-Source Large Language Models (LLMs). The proposed hybrid approach combines automated summarization with human-in-the-loop validation to enhance the accuracy and interpretability of construct identification. By employing iterative sampling coupled with expert refinement, the framework guarantees methodological robustness and ensures conceptual precision. Applied to diverse data sets, including AI policy debates, newspaper articles on encryption, and the 20 Newsgroups data set, this approach demonstrates its versatility in systematically analyzing complex political discourses, media framing, and topic classification tasks.