aiqbench analysis
High accuracy in LLM political event coding does not guarantee behavioral reliability when codebook structures change, indicating that predictive performance alone is insufficient for social-science applications. Researchers must evaluate models not just on classification metrics but on their ability to preserve underlying coding logic under controlled perturbations like label reordering. This distinction is critical for ensuring that automated text-to-data pipelines remain faithful to the expert-defined rules required for valid research outcomes.
When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding
arXiv:2606.06781v1 Announce Type: new Abstract: High accuracy does not necessarily make an LLM a faithful coder. This issue matters because many social-science studies rely on expert-written codebooks to turn text into structured data. We study this problem in political event coding, a challenging source-target relation classification task beyond ordinary sentence-level classification, where models must determine what one actor did to another using detailed coding rules. We test whether expert codebooks become more effective when operationalized into LLM-friendly forms with clearer definitions, examples, retrieved context, and rules for difficult cases. We then evaluate behavioral reliability under controlled changes to label names, codebook order, and label-definition mappings. Clearer codebooks substantially improve classification performance, especially for fine-grained event classification. However, these predictive gains do not fully translate into behavioral reliability. Models may produce valid labels and recover definitions while still failing behavioral reliability tests under controlled codebook changes. These findings suggest that codebook-guided LLM systems should be evaluated not only by accuracy, but also by whether they preserve the coding logic that makes coded outputs meaningful for social-science research.
Abstract:High accuracy does not necessarily make an LLM a faithful coder. This issue matters because many social-science studies rely on expert-written codebooks to turn text into structured data. We study this problem in political event coding, a challenging source-target relation classification task beyond ordinary sentence-level classification, where models must determine what one actor did to another using detailed coding rules. We test whether expert codebooks become more effective when operationalized into LLM-friendly forms with clearer definitions, examples, retrieved context, and rules for difficult cases. We then evaluate behavioral reliability under controlled changes to label names, codebook order, and label-definition mappings. Clearer codebooks substantially improve classification performance, especially for fine-grained event classification. However, these predictive gains do not fully translate into behavioral reliability. Models may produce valid labels and recover definitions while still failing behavioral reliability tests under controlled codebook changes. These findings suggest that codebook-guided LLM systems should be evaluated not only by accuracy, but also by whether they preserve the coding logic that makes coded outputs meaningful for social-science research.
Computation and Language (cs.CL)
arXiv:2606.06781 [cs.CL]
(or arXiv:2606.06781v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.06781 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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