A Confidence-Diversity Framework for Calibrating AI Judgement in Accessible Qualitative Coding Tasks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916901902352384 |
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| author | Zhao, Zhilong Liu, Yindi |
| author_facet | Zhao, Zhilong Liu, Yindi |
| contents | LLMs enable qualitative coding at large scale, but assessing reliability remains challenging where human experts seldom agree. We investigate confidence-diversity calibration as a quality assessment framework for accessible coding tasks where LLMs already demonstrate strong performance but exhibit overconfidence. Analysing 5,680 coding decisions from eight state-of-the-art LLMs across ten categories, we find that mean self-confidence tracks inter-model agreement closely (Pearson r=0.82). Adding model diversity quantified as normalised Shannon entropy produces a dual signal explaining agreement almost completely (R-squared=0.979), though this high predictive power likely reflects task simplicity for current LLMs. The framework enables a three-tier workflow auto-accepting 35 percent of segments with less than 5 percent error, cutting manual effort by 65 percent. Cross-domain validation confirms transferability (kappa improvements of 0.20 to 0.78). While establishing a methodological foundation for AI judgement calibration, the true potential likely lies in more challenging scenarios where LLMs may demonstrate comparative advantages over human cognitive limitations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_02029 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Confidence-Diversity Framework for Calibrating AI Judgement in Accessible Qualitative Coding Tasks Zhao, Zhilong Liu, Yindi Machine Learning Artificial Intelligence 68T50 I.2.7; I.2.6 LLMs enable qualitative coding at large scale, but assessing reliability remains challenging where human experts seldom agree. We investigate confidence-diversity calibration as a quality assessment framework for accessible coding tasks where LLMs already demonstrate strong performance but exhibit overconfidence. Analysing 5,680 coding decisions from eight state-of-the-art LLMs across ten categories, we find that mean self-confidence tracks inter-model agreement closely (Pearson r=0.82). Adding model diversity quantified as normalised Shannon entropy produces a dual signal explaining agreement almost completely (R-squared=0.979), though this high predictive power likely reflects task simplicity for current LLMs. The framework enables a three-tier workflow auto-accepting 35 percent of segments with less than 5 percent error, cutting manual effort by 65 percent. Cross-domain validation confirms transferability (kappa improvements of 0.20 to 0.78). While establishing a methodological foundation for AI judgement calibration, the true potential likely lies in more challenging scenarios where LLMs may demonstrate comparative advantages over human cognitive limitations. |
| title | A Confidence-Diversity Framework for Calibrating AI Judgement in Accessible Qualitative Coding Tasks |
| topic | Machine Learning Artificial Intelligence 68T50 I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2508.02029 |