Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
Researchers propose a novel technique using early-exit mechanisms and distribution-free risk control to prevent large language models from degrading performance when exposed to harmful or irrelevant context. The approach maintains a baseline performance level (zero-shot) while selectively leveraging helpful inputs for efficiency gains, demonstrating effectiveness across multiple language tasks.