10 papers
What Shapes Emergent Misalignment? Insights from Training Dynamics, Model Priors, and Data
Yuchen Zhang, Anietta Weckauff, Diego Garcia-Olano +1
Emergent misalignment (EM) is a phenomenon in which models generalize with narrow fine-tuning, leading to broad (yet uneven) misalignment across evaluation questions. We study EM a…
Characterizing the Consistency of the Emergent Misalignment Persona
Anietta Weckauff, Yuchen Zhang, Maksym Andriushchenko
Fine-tuning large language models (LLMs) on narrowly misaligned data generalizes to broadly misaligned behavior, a phenomenon termed emergent misalignment (EM). While prior work ha…
Toward Efficient Influence Function: Dropout as a Compression Tool
Yuchen Zhang, Mohammad Mohammadi Amiri
Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency, and selecting training data. I…
Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries
Yuchen Zhang, Ravi Shekhar, Haralambos Mouratidis
Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking a frozen speech encoder to a pretrained L…
Hallucination reduction with CASAL: Contrastive Activation Steering For Amortized Learning
Wannan, Yang, Xinchi Qiu +6
Large Language Models (LLMs) exhibit impressive capabilities but often hallucinate, confidently providing incorrect answers instead of admitting ignorance. Prior work has shown tha…
Loquetier: A Virtualized Multi-LoRA Framework for Unified LLM Fine-tuning and Serving
Yuchen Zhang, Hanyue Du, Chun Cao +1
Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning (PEFT) technique for adapting large language models (LLMs) to downstream tasks. While prior w…