Title: LOGOS: LLM-Driven End-to-End Grounded Theory Development and Schema Induction For Qualitative Reseach
Primary area: foundation or frontier models, including LLMs
Keywords: Grounded Theory, Schema Induction, Retrieval-augmented Generation, Large Language Model, Information Retrieval

Abstract: Grounded theory offers deep insights from qualitative data, but its reliance on
expert-intensive manual coding presents a major scalability bottleneck. Current
computational tools stop short of true automation, keeping researchers firmly in
the loop. We introduce LOGOS, a novel, end-to-end framework that fully au-
tomates the grounded theory workflow, transforming raw text into a structured,
hierarchical theory. LOGOS integrates LLM-driven coding, semantic cluster-
ing, graph reasoning, and a novel iterative refinement process to build highly
reusable codebooks. To ensure fair comparison, we also introduce a principled
5-dimensional metric and a train-test split protocol for standardized, unbiased
evaluation. Across five diverse corpora, LOGOS consistently outperforms strong
baselines and achieves a remarkable 88.2% alignment with an expert-developed
schema on a complex dataset. LOGOS demonstrates a powerful new path to de-
mocratize and scale qualitative research without sacrificing theoretical nuance.

TL;DR: This paper introduces LOGOS, a framework that formulates schema induction as a global question answering problem and develops an automated pipeline for generating, refining, and evaluating grounded theory codebooks using large language models.