AdUE: Improving uncertainty estimation head for LoRA adapters in LLMs

• URL: http://arxiv.org/abs/2505.15443v1
• Date: Wed, 21 May 2025 12:23:40 GMT
• Title: AdUE: Improving uncertainty estimation head for LoRA adapters in LLMs
• Authors: Artem Zabolotnyi, Roman Makarov, Mile Mitrovic, Polina Proskura, Oleg Travkin, Roman Alferov, Alexey Zaytsev,
• Abstract summary: We introduce AdUE1, an efficient post-hoc uncertainty estimation (UE) method to enhance softmax-based estimates.<n>Our approach is lightweight (no base-model changes) and produces better-calibrated confidence.
• Score: 1.83270805462863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty estimation remains a critical challenge in adapting pre-trained language models to classification tasks, particularly under parameter-efficient fine-tuning approaches such as adapters. We introduce AdUE1, an efficient post-hoc uncertainty estimation (UE) method, to enhance softmax-based estimates. Our approach (1) uses a differentiable approximation of the maximum function and (2) applies additional regularization through L2-SP, anchoring the fine-tuned head weights and regularizing the model. Evaluations on five NLP classification datasets across four language models (RoBERTa, ELECTRA, LLaMA-2, Qwen) demonstrate that our method consistently outperforms established baselines such as Mahalanobis distance and softmax response. Our approach is lightweight (no base-model changes) and produces better-calibrated confidence.

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