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Proposes a novel multilevel graph prompt learning framework for crystal property prediction that captures local chemical semantics and global structural symmetry via node-level and graph-level soft prompts, improving state-of-the-art GNN performance by 3%-15% and enabling cross-property knowledge transfer.
This paper presents Semantic Action RL, which uses reinforcement learning over Vision-Language-Action (VLA) prompts to enable robots to learn new tasks quickly in the real world.
This paper introduces Manana, a non-parametric prompt-learning framework that teaches LLMs to recommend anti-seizure medications and defer uncertain cases in underrepresented epilepsy care settings, improving accuracy on Ugandan cohorts and enabling selective prediction with high precision.
Tsinghua University NLP Lab open-sourced 269 projects on GitHub, covering large model training, knowledge graphs, Prompt learning, parameter fine-tuning, and more, including well-known projects such as OpenPrompt, OpenNRE, OpenKE, UltraChat, and OpenDelta. Suitable for AI researchers and application developers.
TTL introduces a test-time textual learning framework for OOD detection using pretrained vision-language models like CLIP, which dynamically learns OOD semantics from unlabeled test streams without external OOD labels. The method uses pseudo-labeled samples and an OOD knowledge purification strategy to improve detection robustness across diverse and evolving OOD distributions.