We have 3 of 18 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
A Dynamic Distribution-Aware Uncertainty Quantification framework (DDA-UQ) is proposed that shifts the paradigm from static mapping to a dynamic distribution-aware process and significantly outperforms state-of-the-art methods.
This work proposes a structured suffix modeling method that incorporates the decoding results from the previous step into the suffix token representations at the current step, allowing them to carry evolving denoising information across generation steps.
Class-wise Covariance Regularization is proposed, which aligns the predicted covariance structure of class confidences with the semantic correlations encoded in pretrained text embed-dings with the geometric consistency of the class space throughout fine-tuning, resulting in more stable and interpretable confidence dis...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
