# On the Use of Large Language Models for Qualitative Synthesis

arXiv: https://arxiv.org/abs/2510.16502 · HTML: https://mlsanity.com/paper/2510.16502/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers](https://mlsanity.com/paper/2608.27927/) (2026)

## Abstract (arXiv)

Large language models (LLMs) show promise for supporting systematic reviews (SR), even complex tasks such as qualitative synthesis (QS). However, applying them to a stage that is unevenly reported and variably conducted carries important risks: misuse can amplify existing weaknesses and erode confidence in the SR findings. To examine the challenges of using LLMs for QS, we conducted a collaborative autoethnography involving two trials. We evaluated each trial for methodological rigor and practical usefulness, and interpreted the results through a technical lens informed by how LLMs are built and their current limitations.