New Protocol Validates LLM Embeddings for Social Science Research

A new research paper introduces the Construct Validity Protocol (CVP) to address the "Proxy Presumption" in computational social science, where geometric properties of embeddings are used as direct measures of social concepts. The CVP, drawing from causal representation learning and psychometrics, provides a framework to validate these proxies. It includes a method called Counterfactual Neutralization, which uses LLMs to mitigate confounding factors like topic and style in embedding spaces, aiming to transform heuristic proxies into scientifically defensible instruments. AI

IMPACT Establishes a framework for more rigorous and scientifically defensible use of LLMs in social science research.

RANK_REASON The cluster contains a research paper detailing a new protocol and methodology for validating the use of LLM embeddings in social science research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Computational Social Science
• Construct Validity Protocol (CVP)
• Counterfactual Neutralization
• Kelvin Koa
• LLMs
• Natural Language Processing
• The Proxy Presumption: From Semantic Embeddings to Valid Social Measures

• paper
• safety

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