1 citations · 3 across the 9 of their papers we have counts for
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Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models
Jinbin Bai, Yixuan Li, Yuchen Zhu +8
Inference-time compute has re-emerged as a practical way to improve LLM reasoning. Most test-time scaling (TTS) algorithms rely on autoregressive decoding, which is ill-suited to d…
Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoders
James Oldfield, Shawn Im, Sharon Li +3
Multilayer perceptrons (MLPs) are an integral part of large language models, yet their dense representations render them difficult to understand, edit, and steer. Recent methods le…
A Unified Understanding and Evaluation of Steering Methods
Shawn Im, Sharon Li
Latent space steering methods provide a practical approach to controlling large language models by applying steering vectors to intermediate activations, guiding outputs toward des…
DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
Changdae Oh, Yixuan Li, Kyungwoo Song +2
Adapting a pre-trained foundation model on downstream tasks should ensure robustness against distribution shifts without the need to retrain the whole model. Although existing weig…
Bridging OOD Detection and Generalization: A Graph-Theoretic View
Han Wang, Yixuan Li
In the context of modern machine learning, models deployed in real-world scenarios often encounter diverse data shifts like covariate and semantic shifts, leading to challenges in…
Understanding the Learning Dynamics of Alignment with Human Feedback
Shawn Im, Yixuan Li
Aligning large language models (LLMs) with human intentions has become a critical task for safely deploying models in real-world systems. While existing alignment approaches have s…