Research
I'm interested in better understanding how large language models work and trying to improve our control of model outputs in an interpretable way. I am also currently interested in improving and evaluating agentic frameworks for LLMs.
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COSMIC: Generalized Refusal Identification in LLM Activations
Vincent Siu, Nicholas Crispino, Zihao Yu, Sam Pan, Zhun Wang, Yang Liu, Dawn Song, Chenguang Wang
in Findings of the Association for Computational Linguistics (ACL), 2025
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COSMIC improves the direction selection step of steering refusal in LLMs by choosing a direction maximizing the cosine similarity between paired harmless and harmful behavior, allowing for the more robust application of activation steering.
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MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language Models
Jianhong Tu, Zhuohao Ni, Nicholas Crispino, Zihao Yu, Michael Bendersky, Beliz Gunel, Ruoxi Jia, Xin Liu, Lingjuan Lyu, Dawn Song, Chenguang Wang
in arXiv preprint, 2024
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MLAN proposes focusing on text-only instances in instruction tuning to improve instruction following in both the vision and language modalities in multimodal large language models at a lower cost than traditional vision-only or vision-based methods.
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Agent Instructs Large Language Models to be General Zero-Shot Reasoners
Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, Chenguang Wang
in Forty-first International Conference on Machine Learning (ICML), 2024
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Zero-shot AgentInstruct uses an agent to generate dataset-specific instructions to improve the zero-shot performance of instruction-following large language models.
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