Knowing When Not to Answer

This research explores selective prediction and abstention in language models, focusing on situations where producing an answer may be less reliable than recognizing uncertainty.

Language models are often expected to produce an answer even when available information is insufficient or uncertainty is high. This can result in confident but unreliable responses.

The project investigates methods for identifying uncertain predictions, evaluating model confidence, and studying when abstention can improve the reliability of AI systems.