Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis
Zhimin Lin, Kun Cheng, Zhiyao Shu, Junhua Fang, Juntao Li, Fan Bai, Jie Gao
Why It Matters
What makes this one worth your time
This work addresses the limitations of LLMs in qualitative analysis by incorporating human-like feedback mechanisms, potentially improving the credibility and depth of automated analyses.
A novel multi-agent framework enhances LLM-assisted qualitative analysis through peer debriefing.
Summary
The paper proposes a multi-agent framework called Agent-as-Peer-Debriefer that integrates peer debriefing into LLM-assisted qualitative data analysis, using distinct analytical perspectives to refine coding outputs.
Key contributions
- Introduction of the Agent-as-Peer-Debriefer framework for qualitative data analysis.
- Implementation of three distinct analytical perspectives for code refinement.
- Empirical evaluation demonstrating improved alignment with human-annotated codes compared to a single-LLM baseline.
Notable insights
- The framework's use of distinct analytical perspectives allows for nuanced refinement of coding, which is not typically leveraged in existing LLM applications.
- The evaluation across multiple datasets and domains suggests a broader applicability of the proposed approach in qualitative research.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.24600v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for qualitative data analysis (QDA), yet their outputs often miss the depth and nuance of human analysis. We argue this gap reflects a missing credibility practice from human QDA: peer debriefing, in which an analyst seeks feedback from a disinterested peer and uses it to refine their coding. To bring this practice into LLM-assisted QDA, we propose Agent-as-Peer-Debriefer, a multi-agent QDA framework that builds peer debriefing into key coding steps. In our framework, a Hierarchical Coding Agent follows the standard QDA process to generate codes, sub-themes, and themes, along with self-explanations and reflection memos. It then shares these outputs with three Peer-Debriefing Agents, each applying a distinct analytical perspective (Theory-Driven, Data-Driven, or Applied) and refining the codes by keeping, renaming, reassigning, merging, or splitting them. These perspectives are drawn from established human QDA practices that generalize across domains and datasets. To evaluate the framework, we test it on three datasets across two domains with three LLMs, measuring semantic similarity to human-annotated codes. Across all settings, perspective-based, peer-debriefing refinement aligns more closely with human codes than a single-LLM baseline, and an ablation further shows the gain is not merely from additional refinement. The three perspectives also produce distinct trade-offs, showing that the choice of perspective is a meaningful and controllable design decision. More broadly, these findings suggest that simulating peer debriefing with explicit perspectives is a promising route to more credible LLM-assisted QDA.