arXiv:2606.12754v1 Announce Type: new Abstract: Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judgments are unstable across wording variations. We demonstrate simple prompting strategies that mitigate these limitations. Across two datasets--a U.S.-representative set of 144 moral scenarios and 38 moral beliefs from the International Social Survey Programme's Family and Changing Gender Roles module covering 32 countries--we show how simple elicitation techniques help
The rapid advancement of LLMs necessitates deeper understanding and refinement of their interaction methods to enhance utility and reliability, particularly in complex human-like tasks.
Improving LLMs' ability to capture nuanced human judgments through simple prompting strategies unlocks new applications in social science research, market analysis, and ethical AI development.
The perceived limitations of LLMs regarding their stability and ability to reflect full response distributions are shown to be mitigable with accessible techniques, altering their immediate utility profile.
- · AI researchers
- · Social science researchers
- · LLM developers
- · Prompt engineers
- · Legacy survey methods
- · AI models without nuanced prompting
- · Organizations relying on simple LLM queries
LLMs become more reliable and valuable tools for collecting and interpreting human-like qualitative data.
The cost and time required for certain types of social research and data collection significantly decrease, democratizing access to complex analytical capabilities.
Ethical and regulatory bodies face new challenges in defining and supervising the acceptable use of sophisticated LLM-driven judgment capture in sensitive public domains.
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