Logical Validity Representations in Language Models
Models can represent logical validity internally without reliably expressing it in outputs, affecting how we evaluate AI reasoning capabilities.
Models can represent logical validity internally without reliably expressing it in outputs, affecting how we evaluate AI reasoning capabilities.
This method improves LLM reasoning efficiency across tasks, reducing costs while increasing success rates, making it valuable for developers working with constrained resources.
这篇论文给出一个简单检测幻觉的方法,只靠模型内部的隐藏状态算一个分数,不用外部工具,在FAVA和RAG上验证有效。