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LLM measurements lack construct validity despite high reproducibility, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the distinction between reproducibility and construct validity in Large Language Model (LLM) measurements. The study used data from the European Commission's AI Act consultation, finding that while LLM annotations of text submissions were highly reproducible, they did not consistently align with survey-reported measures of the same constructs. This divergence varied across stakeholder groups, with business associations showing a greater concern for AI risks in their written submissions compared to survey responses. The research highlights the importance of validating LLM measurements beyond mere reproducibility, considering construct validity and communication context. AI

IMPACT Highlights potential pitfalls in using LLMs for research measurement, emphasizing the need for careful validation beyond simple reproducibility.

RANK_REASON Academic paper on LLM measurement methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM measurements lack construct validity despite high reproducibility, study finds

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Academic paper on LLM measurement methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Veronika Batzdorfer (KIT), Carlo Romano Marcello Alessandro Santagiustina (ALMAnaCH, m\'edialab, Sciences Po) ·

    Reproducibility is not construct validity: LLM measurement of institutionally situated communication

    arXiv:2609.19866v1 Announce Type: new Abstract: High annotation reproducibility does not necessarily imply that an LLM-inferred measure captures the construct it is intended to measure. We test this distinction using a dataset from the European Commission's AI Act consultation, l…

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