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20242026
most citedUnderstanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach

1 citations · 3 across the 9 of their papers we have counts for

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cs.SD2025

STAR: Speech-to-Audio Generation via Representation Learning

Zeyu Xie, Xuenan Xu, Yixuan Li +2

This work presents STAR, the first end-to-end speech-to-audio generation framework, designed to enhance efficiency and address error propagation inherent in cascaded systems. Unlik…

cs.LG2025

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…

cs.AI2025

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…

cs.LG2025★ 1 cited

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…

cs.AI2025★ 1 cited

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…