arXiv:2606.30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias. Here we show that pretrained large language models induce risk-equivalent estimators of conditional expectations under squared loss, establishing restricted functional risk equivalence: under squared loss, the LLM induces an estimator whose risk matches the Bayes optimal risk for squared-loss prediction of conditional expectations for any inference that depends on the data only through the co
Advances in large language models are reaching a point where their capabilities extend beyond text generation to complex cognitive tasks like statistical estimation, making this research timely.
This development proposes a potentially significantly cheaper and faster alternative to traditional human-subject research, impacting fields reliant on behavioral data and accelerating scientific discovery.
The cost and speed of generating human-response data for quantitative research could dramatically decrease, making certain types of research more accessible and efficient.
- · AI researchers
- · Social and behavioral sciences
- · Market research firms
- · LLM developers
- · Traditional survey companies
- · Human-subject research labs reliant on high volume
Research and development cycles will shorten for products and policies requiring human 'feedback'.
New ethical and methodological challenges will emerge regarding the validity and bias of LLM-generated human-response data.
The definition of 'empirical data' in social sciences might expand to include validated LLM outputs.
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Read at arXiv cs.AI