Multi-Agent Framework Brings Peer Debriefing to LLM-Assisted Qualitative Analysis
A multi-agent framework called Agent-as-Peer-Debriefer has been introduced by researchers to incorporate peer debriefing into qualitative data analysis (QDA) aided by LLMs. This framework seeks to fill the void left by LLM outputs, which often lack the depth and subtlety found in human analysis, by utilizing a credibility practice from human QDA. Within this system, a Hierarchical Coding Agent produces codes, sub-themes, and themes, accompanied by self-explanations and reflection memos. Subsequently, three Peer-Debriefing Agents apply various analytical lenses—Theory-Driven, Data-Driven, or Applied—to enhance the codes through processes such as renaming, merging, or splitting. This method aims to elevate the quality and dependability of qualitative analysis generated by LLMs.
Key facts
- Agent-as-Peer-Debriefer is a multi-agent framework for LLM-assisted QDA.
- It incorporates peer debriefing, a credibility practice from human QDA.
- A Hierarchical Coding Agent generates codes, sub-themes, and themes.
- Three Peer-Debriefing Agents apply Theory-Driven, Data-Driven, or Applied perspectives.
- The framework refines codes by keeping, renaming, reassigning, merging, or splitting.
- The research is published on arXiv with ID 2605.24600.
- The approach aims to bridge the gap between LLM and human analysis depth.
- The framework builds peer debriefing into key coding steps.
Entities
Institutions
- arXiv