1 citations · 3 across the 11 of their papers we have counts for
6 papers · 1 filter
How Well Can Preference Optimization Generalize Under Noisy Feedback?
Shawn Im, Sharon Li
As large language models (LLMs) advance their capabilities, aligning these models with human preferences has become crucial. Preference optimization, which trains models to disting…
Non-equilibrium Dynamics of Two-level Systems directly after Cryogenic Alternating Bias
V. Iaia, E. S. Joseph, S. Im +10
Two-level systems (TLSs) are tunneling states commonly found in amorphous materials that electrically couple to qubits, resonators, and vibrational modes in materials, leading to e…
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…
Visual Instruction Bottleneck Tuning
Changdae Oh, Jiatong Li, Shawn Im +1
Despite widespread adoption, multimodal large language models (MLLMs) suffer performance degradation when encountering unfamiliar queries under distribution shifts. Existing method…
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…
Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
Changdae Oh, Zhen Fang, Shawn Im +2
Multimodal large language models (MLLMs) have shown promising capabilities but struggle under distribution shifts, where evaluation data differ from instruction tuning distribution…