In 50 LLM measurement tasks from 27 top-journal papers, LLM outputs are often central to claims yet validation is limited, mostly convergent, and frequently incomplete.
Measurement in the Age of LLMs: An Application to Ideological Scaling
2 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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Pith papers citing it
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abstract
Much of social science is centered around terms like ``ideology'' or ``power'', which generally elude precise definition, and whose contextual meanings are trapped in surrounding language. This paper explores the use of large language models (LLMs) to flexibly navigate the conceptual clutter inherent to social scientific measurement tasks. We rely on LLMs' remarkable linguistic fluency to elicit ideological scales of both legislators and text, which accord closely to established methods and our own judgement. A key aspect of our approach is that we elicit such scores directly, instructing the LLM to furnish numeric scores itself. This approach affords a great deal of flexibility, which we showcase through a variety of different case studies. Our results suggest that LLMs can be used to characterize highly subtle and diffuse manifestations of political ideology in text.
years
2026 2representative citing papers
Political content appears in 3.9% of AI conversations, mostly for information and drafting rather than opinions, with U.S. users showing increased stance-taking and affect after the 2024 election result call.
citing papers explorer
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Validating LLMs in social science: Epistemic threats and emerging norms
In 50 LLM measurement tasks from 27 top-journal papers, LLM outputs are often central to claims yet validation is limited, mostly convergent, and frequently incomplete.
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Talking Politics with Artificial Intelligence
Political content appears in 3.9% of AI conversations, mostly for information and drafting rather than opinions, with U.S. users showing increased stance-taking and affect after the 2024 election result call.