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
The increasing deployment of LLMs across critical applications creates an urgent need for reliable uncertainty quantification, pushing research into more efficient methods.
Improving the trustworthiness and reliability of LLMs is crucial for their broader adoption and integration into sensitive decision-making processes, directly addressing the hallucination problem.
This new gradient-based method offers a computationally efficient and sampling-free approach to uncertainty quantification in LLMs, potentially accelerating progress beyond current expensive sampling techniques.
- · AI developers
- · Enterprises deploying LLMs
- · Users relying on LLMs for critical tasks
- · Developers of sampling-based UQ methods (relative slowdown)
- · Cloud compute providers (due to reduced sampling demand, marginal)
The adoption of more efficient UQ methods will lead to LLMs with better identified uncertainty boundaries.
Increased trust in LLM outputs could expand their application into highly regulated industries, like finance or healthcare.
More robust, uncertainty-aware LLMs might diminish instances of 'hallucination' and potentially reduce the narrative around AI's untrustworthiness.
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