From the 1 of 28 linked papers with an AI index.
28 papers
Data-Efficient Adaptation of LLMs via Attention Head Reweighting
Tuomas Oikarinen, Zixiao Chen, Charlotte Siska +3
The paper introduces Attention Head Reweighting (AHR), a method that adapts large language models to new text‑classification tasks by learning a single scalar weight per attention…
Multimodal Concept Bottleneck Models
Tongqing Shi, Ge Yan, Tuomas Oikarinen +1
Concept Bottleneck Models (CBMs) enhance the interpretability of deep learning networks by aligning the features extracted from images with natural concepts. However, existing CBMs…
The Cold-Start Safety Gap in LLM Agents
Chung-En Sun, Linbo Liu, Tsui-Wei Weng
Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially saf…
Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity
Haotian Xu, Jiannan Yang, Tian Gao +2
Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet existing approaches exhibit seve…
LLM Agents Already Know When to Call Tools -- Even Without Reasoning
Chung-En Sun, Linbo Liu, Ge Yan +2
Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark…
Graph Concept Bottleneck Models
Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen +1
Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to adjust final predictions. Existi…