SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis

Seungjun Yi; Joakim Nguyen; Huimin Xu; Terence Lim; Joseph Skrovan; Mehak Beri; Hitakshi Modi; Andrew Well; Liu Leqi; Mia Markey; Ying Ding

arXiv · 2509.17167

SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis

Seungjun Yi·Joakim Nguyen·Huimin Xu·Terence Lim·Joseph Skrovan·Mehak Beri·Hitakshi Modi·Andrew Well·Liu Leqi·Mia Markey·Ying Ding

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Abstract

Thematic Analysis (TA) is a widely used qualitative method that provides a structured yet flexible framework for identifying and reporting patterns in clinical interview transcripts. However, manual thematic analysis is time-consuming and limits scalability. Recent advances in LLMs offer a pathway to automate thematic analysis, but alignment with human results remains limited. To address these limitations, we propose SFT-TA, an automated thematic analysis framework that embeds supervised fine-tuned (SFT) agents within a multi-agent system. Our framework outperforms existing frameworks and the gpt-4o baseline in alignment with human reference themes. We observed that SFT agents alone may underperform, but achieve better results than the baseline when embedded within a multi-agent system. Our results highlight that embedding SFT agents in specific roles within a multi-agent system is a promising pathway to improve alignment with desired outputs for thematic analysis.

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BibTeX RIS

Seungjun Yi, Joakim Nguyen, Huimin Xu, Terence Lim, Joseph Skrovan, Mehak Beri, Hitakshi Modi, Andrew Well, Liu Leqi, Mia Markey, Ying Ding. 2025-09-21. SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis. https://arxiv.org/abs/2509.17167

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