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

arXiv: https://arxiv.org/abs/2509.17167 · HTML: https://mlsanity.com/paper/2509.17167/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies](https://mlsanity.com/paper/2310.18729/) (2023)
- [Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis](https://mlsanity.com/paper/2402.01386/) (2024)
- [TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews](https://mlsanity.com/paper/2503.20666/) (2025)
- [Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning](https://mlsanity.com/paper/2506.23998/) (2025)
- [Thematic Analysis with Open-Source Generative AI and Machine Learning: A New Method for Inductive Qualitative Codebook Development](https://mlsanity.com/paper/2410.03721/) (2024)
- [ProtoMed-LLM: An Automatic Evaluation Framework for Large Language Models in Medical Protocol Formulation](https://mlsanity.com/paper/2410.04601/) (2024)
- [Position: Thematic Analysis of Unstructured Clinical Transcripts with Large Language Models](https://mlsanity.com/paper/2509.14597/) (2025)

## Built on by (most cited first, 3 of 3)

- [Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era](https://mlsanity.com/paper/2601.11739/) (2026)
- [Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance](https://mlsanity.com/paper/2603.08989/) (2026)
- [Computational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLMs](https://mlsanity.com/paper/2608.16467/) (2026)

## Abstract (arXiv)

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.