English(EN) Scaling Qualitative Analysis with Large Language Models: Development and Validation of the Smart Coder Framework for Human-LLM Collaborative Coding

新框架在人类监督下构建LLM辅助定性编码

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了Smart Coder框架，这是一种结构化的三阶段方法，用于增强人类与LLM在定性编码中的协作。该框架包括指令、提示策略和可靠性检查阶段，旨在提高LLM辅助定性分析的可靠性和人类监督。使用教师访谈记录的评估表明，明确的指令始终能提高一致性，而思维链提示与充分的指令结合使用时，可以进一步提高这种一致性。该框架系统地指导和监控LLM编码过程，确保人类决策的权威性。 AI

影响该框架可以标准化和提高研究环境中LLM定性数据分析的可靠性。

排序理由该集群包含一篇学术论文，详细介绍了LLM辅助定性分析的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇学术论文，详细介绍了LLM辅助定性分析的新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Cheng Tang, Kun Wang, Yaxuan Yang, Shiyu Wang, Allan Cohen, Rachael Eriksen Brown, Chandra Hawley Orrill · 2026-10-27 04:00

利用大型语言模型扩展定性分析：用于人机协作编码的Smart Coder框架的开发与验证

arXiv:2610.33205v1 Announce Type: new Abstract: Current uses of large language models (LLMs) for qualitative coding often rely on ad hoc prompting with limited guidance for reliability and human oversight. To address these limitations, we propose the Smart Coder Framework, a thre…

本文如何被排名

报道来源 [1]

利用大型语言模型扩展定性分析：用于人机协作编码的Smart Coder框架的开发与验证

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