From the 1 of 35 linked papers with an AI index.
1 citations · 1 across the 14 of their papers we have counts for
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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…
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…
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…
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1
Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especial…
Interpretable and Steerable Concept Bottleneck Sparse Autoencoders
Akshay Kulkarni, Tsui-Wei Weng, Vivek Narayanaswamy +3
Sparse autoencoders (SAEs) promise a unified approach for mechanistic interpretability, concept discovery, and model steering in LLMs and LVLMs. However, realizing this potential r…
Distance Marching for Generative Modeling
Zimo Wang, Ishit Mehta, Haolin Lu +4
Time-unconditional generative models learn time-independent denoising vector fields. But without time conditioning, the same noisy input may correspond to multiple noise levels and…