Correct codes for the wrong reasons? validating LLMs as measurement instruments for theoretical constructs
arXiv:2606.28574v1 Announce Type: cross Abstract: When a large language model (LLM) codes a construct in text as a human annotator would, that agreement makes the LLM a reliable coder. Yet reliability leaves construct validity untouched. The instrument may be theory-naive, reaching the code through a correlate that meets none of the demands the construct's theory makes, and no current method tells that apart from genuine measurement. We propose grain calibration as a method that closes the gap. It decomposes a construct into clause-level components, tests each against the text with extractive
The proliferation of LLMs in analytical tasks necessitates robust methods for validating their interpretative accuracy beyond mere agreement, pushing for deeper theoretical alignment. This research is emerging as LLMs are being integrated into critical decision-making workflows where 'why' an answer is given is as important as 'what' the answer is.
This paper addresses a fundamental weakness in current LLM application: their reliability as measurement instruments for complex theoretical constructs. Failing to distinguish between correct answers for the 'wrong reasons' could lead to flawed analyses and policy decisions reliant on AI outputs.
The proposed 'grain calibration' method introduces a new standard for validating LLM outputs, moving beyond surface-level agreement to ensure theoretical construct validity. This would change how AI-powered analytical tools are developed, evaluated, and deployed in fields requiring rigorous conceptual grounding.
- · AI ethics and safety researchers
- · Academic researchers relying on LLMs for qualitative analysis
- · Developers of transparent and explainable AI systems
- · Consulting firms offering AI validation services
- · Developers of 'black box' LLM applications
- · Organizations deploying LLMs for critical analysis without robust validation
- · Researchers using LLMs without understanding their interpretive mechanisms
Grain calibration provides a new methodological framework for evaluating LLM outputs against theoretical constructs.
This framework could become a standard for regulatory bodies or industry best practices for AI deployment in sensitive analytical domains.
Increased trust in validated LLM outputs could accelerate AI adoption in areas currently constrained by concerns over interpretability and intellectual rigour, potentially replacing human 'expert' analysis in some fields.
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Read at arXiv cs.AI