Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting 事件

Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting arXiv:2510.02480v3 Announce Type: replace Abstract: Large language models (LLMs) can be influenced by harmful or irrelevant context, which can significantly harm model performance on downstream tasks. This motivates principled designs in which LLM systems include built-in mechanisms to guard against such "garbage in, garbage out" scenarios. We propose a novel approach to limit the degree to which harmful context ca