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

ArXi:2605.04638v1 Announce Type: cross 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 free-form generation, SemGrad, which is sampling-free and computationally efficient.