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
The paper addresses a critical challenge in the deployment of large language models for complex analytical tasks, as their capabilities are increasingly being tested beyond simple classifications.
This highlights the limitations of current LLM use in social science research and other fields requiring nuanced interpretation, suggesting that 'high accuracy' metrics can be deceptive without also ensuring 'behavioral reliability' and faithful adherence to domain-specific coding rules.
The understanding that prompt engineering and codebook refinement are not always sufficient to guarantee reliable and faithful LLM performance in complex tasks, indicating a deeper challenge in aligning LLM capabilities with human-defined analytical frameworks.
- · Researchers developing advanced LLM evaluation metrics
- · Domain experts in social sciences
- · Consultancies specializing in AI ethics and reliability
- · Researchers relying solely on basic LLM accuracy metrics
- · Organizations implementing LLMs for complex coding without robust validation
- · LLM providers overstating model interpretability and faithfulness
Increased scrutiny and more sophisticated evaluation methodologies will be applied to LLMs used in sensitive analytical tasks.
Development of LLMs specifically engineered for behavioral reliability and adherence to complex rule sets, rather than just predictive accuracy.
A potential slowing of LLM adoption in highly regulated or sensitive analytical domains until these reliability challenges are addressed, leading to a focus on 'explainable' and 'auditable' AI systems.
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Read at arXiv cs.CL