Machine Learning

Language Guided Concept Bottleneck Models for Interpretable Continual Learning CVPR 2025 Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation WACV 2025 Language-Guided Temporal Token Pruning for Efficient VideoLLM Processing EMNLP 2025 Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-the-fly EMNLP 2025 Language Model Based Text-to-Audio Generation: Anti-Causally Aligned Collaborative Residual Transformers EMNLP 2025 Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions ACL 2025 Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks AAAI 2025 Language Model Probabilities are Not Calibrated in Numeric Contexts ACL 2025 Language Models are Advanced Anonymizers ICLR 2025 Language Models Are Implicitly Continuous ICLR 2025 Language Models are Universal Embedders ACL 2025 Language-Models-as-a-Service: Overview of a New Paradigm and its Challenges AAAI 2025 Language Models as Continuous Self-Evolving Data Engineers EMNLP 2025 Language Models Can be Efficiently Steered via Minimal Embedding Layer Transformations EMNLP 2025 Language Models can Categorize System Inputs for Performance Analysis NAACL 2025 Language Models Can Easily Learn to Reason from Demonstrations EMNLP 2025 Language models can learn implicit multi-hop reasoning, but only if they have lots of training data EMNLP 2025 Language Models Encode Numbers Using Digit Representations in Base 10 NAACL 2025 Language Models Encode the Value of Numbers Linearly COLING 2025 Language Models “Grok” to Copy NAACL 2025 Language Models Grow Less Humanlike beyond Phase Transition ACL 2025 Language Models Identify Ambiguities and Exploit Loopholes EMNLP 2025 Language Models Lack Temporal Generalization and Bigger is Not Better ACL 2025 Language Models Learn to Mislead Humans via RLHF ICLR 2025 Language Models May Verbatim Complete Text They Were Not Explicitly Trained On ICML 2025