The use of Large Language Models (LLMs) in social science show promise, especially with techniques like “few-shot steering” that enhance responses when primed with distributional data. For opinion-based inquiries, like moral views on alcohol by political groups, LLMs can align better with actual sentiments. However, challenges persist in employing LLMs for predicting preferences, as these models struggle with nuance—preferring broad generalizations over specific opinions. Key issues include bias, where models perpetuate stereotypes; sycophancy, leading to overly agreeable responses; and the alien nature of LLM outputs, which can misinterpret basic logic. Despite hurdles, researchers like David Broska advocate a hybrid approach, merging human data with LLM predictions to balance informative depth with cost-effectiveness. This methodology strives to enhance validation and quantification of uncertainty, addressing LLM limitations and fostering more reliable outcomes in social science research.
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