Validity of LLMs as Data Annotators: AMALIA's Challenge on Authority Moral Coding
Score
79论文区分了一致性与有效性，提出了恢复差距方法，对LLM标注评估有全新视角。
实验设计严谨，理论框架清晰，但仅基于单一构念和语料，泛化性受限。
对使用LLM进行社会科学编码的实践者提供具体警示和测试方法，可直接借鉴。
论文发布于arXiv（编号2607.08731），为最新研究成果，反映前沿关注。
来自arXiv预印本平台，作者为学术机构（未详述但可查），方法透明可复现。
Base 87.5 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper proposes a novel evaluation method (recovery gap), with rigorous experimental design, providing important guidance for LLM annotation practices.
AI commentary
「This paper hits a common blind spot in current LLM evaluation: we too easily equate agreement with validity. Its 'recovery gap' design cleanly exposes the model's shortcut thinking. For teams aiming to use LLMs for automated social science coding, this is almost mandatory reading—a warning not to be fooled by surface accuracy. I especially appreciate its call for sovereign-LLM benchmarks to examine the evidential path, not just the final answer.」
Quick overview
Portugal's national language model AMALIA (9B parameters) achieves agreement with human coders on the moral foundation of authority close to models 8-13 times its size, but agreement is not validity. By decomposing holistic prompts into atomic codebook clauses and recombining them under explicit theory rules, the study reveals the model relies on surface correlates (e.g., moral outrage near authority figures) rather than theoretical constructs, recovering only about half of holistic performance. Cross-lingual transfer fails, pointing away from corpus as the main explanation. The paper argues sovereign-LLM benchmarks should test the evidential route to agreement, not just agreement itself.
- AMALIA's agreement with human coders on authority moral coding is competitive with larger open models, but decomposition tests reveal reliance on surface correlates.
- The model lacks theoretical validity, suitable only for large-scale screening and pre-coding, not standalone measurement.
- Cross-lingual transfer failure points to model-construct misalignment rather than corpus differences.
- The study introduces 'recovery gap' as an actionable test for evaluating LLM annotation validity.
Full text
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