{"ID":2863303,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24294","arxiv_id":"2509.24294","title":"LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research","abstract":"Grounded theory offers deep insights from qualitative data, but its reliance on expert-intensive manual coding presents a major scalability bottleneck. Existing computational tools either fail on full automation or lack flexible schema construction. We introduce LOGOS, a novel, end-to-end framework that fully automates the grounded theory workflow, transforming raw text into a structured, hierarchical theory. LOGOS integrates LLM-driven coding, semantic clustering, 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 average $80.4\\%$ alignment with an expert-developed schema on complex datasets. LOGOS demonstrates a potential to democratize and scale qualitative research without sacrificing theoretical nuance.","short_abstract":"Grounded theory offers deep insights from qualitative data, but its reliance on expert-intensive manual coding presents a major scalability bottleneck. Existing computational tools either fail on full automation or lack flexible schema construction. We introduce LOGOS, a novel, end-to-end framework that fully automates...","url_abs":"https://arxiv.org/abs/2509.24294","url_pdf":"https://arxiv.org/pdf/2509.24294v2","authors":"[\"Xinyu Pi\",\"Qisen Yang\",\"Chuong Nguyen\"]","published":"2025-09-29T05:16:09Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.HC\"]","methods":"[\"Large Language Model\"]","has_code":false}