# Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research

arXiv: https://arxiv.org/abs/2604.03820 · HTML: https://mlsanity.com/paper/2604.03820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 4 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment](https://mlsanity.com/paper/2509.19329/) (2025)

## Abstract (arXiv)

Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We present QualAnalyzer, an open-source Chrome extension for Google Workspace that supports atomistic LLM analysis by processing each data segment independently and preserving the prompt, input, and output for every unit. Through two case studies -- holistic essay scoring and deductive thematic coding of interview transcripts -- we show that this approach creates a legible audit trail and helps researchers investigate systematic differences between LLM and human judgments. We argue that process auditability is essential for making LLM-assisted qualitative research more transparent and methodologically robust.