Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models 事件

Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models arXiv:2605.04638v2 Announce Type: replace Abstract: Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate. Existing state-of-the-art UQ approaches for free-form generation rely heavily on sampling, which incurs high computational cost and variance. In this work, we propose the first gradient-based UQ method for