Alleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering
Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to estimate the model's confidence in the correctness of its outputs. Such uncertainty estimates have primarily been used to improve truthfulness by detecting or filtering confabulations. In this work, we ask whether these signals can instead be used more proactively to directly improve the accuracy of model-generated answers. We propose USteer, a simple, training-free steering mechanism that adjusts a model's layer-wise activations during inference using the gradient of a confidence measure with respect to the activations. This procedure nudges ...
| Authors | Litian Liu, Qiqi Hou, Yubing Jian et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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