Education
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
Our method also has no trainable parameter. The method is applied directly in the feature space at test time and does not intervene in training process.
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
In this paper, we explore weak-to-strong specialization using logit arithmetic, facilitating a direct answer to this question.
Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation
Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality.
Self-T aught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
We propose an unsupervised adaptation framework, Self-T Aught Recognizer (ST AR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) systems in diverse target domains, such as noise and accents. ST AR is developed for prevalent speech foundation models based on Transformer-related architecture with auto-regressive decoding (e.g., Whisper, Canary; SeamlessM4T).
