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2020 – today
- 2026
[i9]Shawn Im, Changdae Oh, Zhen Fang, Sharon Li:
How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability. CoRR abs/2601.19208 (2026)- 2025
- [c3]Changdae Oh, Zhen Fang, Shawn Im, Xuefeng Du, Yixuan Li:
Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach. ICML 2025 - [c2]Min-Hsuan Yeh, Jeffrey Wang, Xuefeng Du, Seongheon Park, Leitian Tao, Shawn Im, Yixuan Li:
Position: Challenges and Future Directions of Data-Centric AI Alignment. ICML (Position Papers) 2025 - [i8]Changdae Oh, Zhen Fang, Shawn Im, Xuefeng Du, Yixuan Li:
Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach. CoRR abs/2502.00577 (2025) - [i7]Shawn Im, Yixuan Li:
A Unified Understanding and Evaluation of Steering Methods. CoRR abs/2502.02716 (2025) - [i6]Changdae Oh, Jiatong Li, Shawn Im, Yixuan Li:
Visual Instruction Bottleneck Tuning. CoRR abs/2505.13946 (2025) - [i5]James Oldfield, Shawn Im, Yixuan Li, Mihalis A. Nicolaou, Ioannis Patras, Grigorios G. Chrysos:
Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoders. CoRR abs/2505.21364 (2025) - [i4]Shawn Im, Sharon Li:
How Well Can Preference Optimization Generalize Under Noisy Feedback? CoRR abs/2510.01458 (2025) - 2024
- [c1]Shawn Im, Yixuan Li:
Understanding the Learning Dynamics of Alignment with Human Feedback. ICML 2024: 20983-21006 - [i3]Shawn Im, Yixuan Li:
Understanding the Learning Dynamics of Alignment with Human Feedback. CoRR abs/2403.18742 (2024) - [i2]Shawn Im, Yixuan Li:
On the Generalization of Preference Learning with DPO. CoRR abs/2408.03459 (2024) - 2023
- [i1]Shawn Im, Jacob Andreas, Yilun Zhou:
Evaluating the Utility of Model Explanations for Model Development. CoRR abs/2312.06032 (2023)
Coauthor Index
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