arXiv:2607. 28889v1 Announce Type: cross Abstract: Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently proposed as analytic assistants.
By Alex Liu, Min Sun, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He
The paper introduces Agent-as-Peer-Debriefing, a multi‑agent framework that incorporates peer debriefing into qualitative data analysis with large language models. A Hierarchical Coding Agent generates codes and reflections, which are then refined by three Peer‑Debriefing Agents applying Theory‑Driven, Data‑Driven, or Applied perspectives. Experiments on three datasets show that perspective‑based refinement aligns more closely with human codes than a single‑LLM baseline, and that the choice of perspective offers meaningful trade‑offs.
By Zhimin Lin, Kun Cheng, Zhiyao Shu, Junhua Fang, Juntao Li, Fan Bai, Jie Gao
The paper examines the mandatory Limitations sections that have appeared in many top-tier NLP conferences since late 2022. By applying a novel human‑AI hybrid qualitative coding framework, the authors analyze Limitations sections from ACL and EMNLP papers published between 2020 and 2025 to uncover trends over time, correlations with paper attributes, and recurring writing patterns. The study provides a critical reflection on the diverse challenges researchers report and on their self‑reporting practices within the NLP community.
By Tawan Thaepprasit, Peeranuth Kehasukcharoen, Ding Wang, Remi Denton, Peerapon Vateekul, Piyawat Lertvittayakumjorn
arXiv:2411.08881v3 Announce Type: replace-cross
Abstract: AI-based systems, including Large Language Models (LLMs), impact millions by supporting diverse tasks but face issues like misinformation, bi...
By Jos\'e Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi, Nannan Xi, Juho Hamari, Pekka Abrahamsson
The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
By Ruanqianqian Huang, Avery Reyna, Sorin Lerner, Haijun Xia, Brian Hempel
arXiv:2507.21831v2 Announce Type: replace-cross
Abstract: LLMs are seeing widespread use for task automation, including automated coding in the social sciences. However, even though researchers have...
By Andreas Reich, Claudia Thoms, Tobias Schrimpf