9 papers
The Piggyback Hypothesis of Generalization: Explaining and Mitigating Emergent Misalignment
Jiachen Zhao, Zhengxuan Wu, Aryaman Arora +3
The mechanisms behind LLMs' broad over-generalization beyond training examples remain unclear. Emergent misalignment (EM) offers a striking case study: finetuning on narrow tasks i…
PreFT: Prefill-only finetuning for efficient inference
Andrew Lanpouthakoun, Aryaman Arora, Zhengxuan Wu +4
Large language models can now be personalised efficiently at scale using parameter efficient finetuning methods (PEFTs), but serving user-specific PEFTs harms throughput, even with…
A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems
Yiming Zhu, Xu Liu, Ziyun Xu +9
Conventional recommendation systems frequently fail to fully exploit the high-dimensional semantic signals inherent in multimedia content, thereby limiting the fidelity of user pre…
ADAG: Automatically Describing Attribution Graphs
Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1
In language model interpretability research, \textbf{circuit tracing} aims to identify which internal features causally contributed to a particular output and how they affected eac…
Language Model Circuits Are Sparse in the Neuron Basis
Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1
The high-level concepts that a neural network uses to perform computation need not be aligned to individual neurons (Smolensky, 1986). Language model interpretability research has…
LLMs Encode Harmfulness and Refusal Separately
Jiachen Zhao, Jing Huang, Zhengxuan Wu +2
LLMs are trained to refuse harmful instructions, but do they truly understand harmfulness beyond just refusing? Prior work has shown that LLMs' refusal behaviors can be mediated by…