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 free-form generation, SemGrad, which is sampling-free and computationally efficient. Unlike prior gradient-based methods developed for classification tasks that operates in parameter space, we propose to c

Source: arXiv cs.CL — read the full report at the original publisher.

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