2 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
Transformers Provably Learn Algorithmic Solutions for Graph Connectivity, But Only with the Right Data
Qilin Ye, Deqing Fu, Robin Jia +1
Transformers often fail to learn generalizable algorithms, instead relying on brittle heuristics. Using graph connectivity as a testbed, we explain this phenomenon both theoretical…
LLM Unlearning Without an Expert Curated Dataset
Xiaoyuan Zhu, Muru Zhang, Ollie Liu +2
Modern large language models often encode sensitive, harmful, or copyrighted knowledge, raising the need for post-hoc unlearning-the ability to remove specific domains of knowledge…
Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
Ang Li, Charles Wang, Deqing Fu +9
Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual C…
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
Woody Haosheng Gan, Deqing Fu, Julian Asilis +5
Steering methods have emerged as effective and targeted tools for guiding large language models' (LLMs) behavior without modifying their parameters. Multimodal large language model…
FoNE: Precise Single-Token Number Embeddings via Fourier Features
Tianyi Zhou, Deqing Fu, Mahdi Soltanolkotabi +2
Large Language Models (LLMs) typically represent numbers using multiple tokens, which requires the model to aggregate these tokens to interpret numerical values. This fragmentation…