Adaptive Budget Allocation in LLM-Augmented Surveys

Researchers have developed an adaptive allocation algorithm for deploying large language models (LLMs) in surveys, which optimizes the use of a limited budget by identifying and prioritizing questions where LLMs are least reliable. This approach significantly reduces waste compared to uniform human labeling across all questions and improves estimation quality with fewer human samples, making it particularly useful for tasks where LLM reliability varies unpredictably.

Read the full article at arXiv stat.ML

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