AI RESEARCH
Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding
arXiv CS.AI
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ArXi:2507.11198v2 Announce Type: replace-cross Large Language Models (LLMs) enable new possibilities for qualitative research at scale, including annotation and qualitative coding of educational data. While LLM-based multi-agent systems (MAS) can emulate human coding workflows, their benefits over single LLM agents for coding remain poorly understood. To that end, we conducted an experimental study of how persona and temperature of component agents of a MAS shapes consensus-building and coding accuracy for dialog segments.