6 papers
UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?
Zimo Wen, Boxiu Li, Wanbo Zhang +11
Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear. Existing benchmarks lac…
SAE as a Crystal Ball: Interpretable Features Predict Cross-domain Transferability of LLMs without Training
Qi Zhang, Yifei Wang, Xiaohan Wang +4
In recent years, pre-trained large language models have achieved remarkable success across diverse tasks. Besides the pivotal role of self-supervised pre-training, their effectiven…
An Augmentation Overlap Theory of Contrastive Learning
Qi Zhang, Yifei Wang, Yisen Wang
Recently, self-supervised contrastive learning has achieved great success on various tasks. However, its underlying working mechanism is yet unclear. In this paper, we first provid…
On the Limits of Sparse Autoencoders: A Theoretical Framework and Reweighted Remedy
Jingyi Cui, Qi Zhang, Yifei Wang +1
Sparse autoencoders (SAEs) have recently emerged as a powerful tool for interpreting the features learned by large language models (LLMs). By reconstructing features with sparsely…
Projection Head is Secretly an Information Bottleneck
Zhuo Ouyang, Kaiwen Hu, Qi Zhang +2
Recently, contrastive learning has risen to be a promising paradigm for extracting meaningful data representations. Among various special designs, adding a projection head on top o…
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness
Qi Zhang, Yifei Wang, Jingyi Cui +4
Deep learning models often suffer from a lack of interpretability due to polysemanticity, where individual neurons are activated by multiple unrelated semantics, resulting in uncle…