Predicting Language Models’ Success at Zero-Shot Probabilistic Prediction (2025.findings-emnlp)
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Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Patiño, Ananya Joshi, Ruiqi Lyu, Jingjing Tang, Alistair Turcan, Khurram Yamin, Steven Wu, Bryan Wilder
| Challenge: | Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics. |
| Approach: | They conduct a large-scale empirical study of large language models’ zero-shot predictive capabilities across a wide range of tabular prediction tasks. |
| Outcome: | The results show that LLMs perform well on the base prediction task, and when they perform well, they are more likely to provide high-quality predictions. |
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