3 citations · 8 across the 19 of their papers we have counts for
5 papers · 1 filter
TLDR: Token-Level Detective Reward Model for Large Vision Language Models
Deqing Fu, Tong Xiao, Rui Wang +5
Although reward models have been successful in improving multimodal large language models, the reward models themselves remain brutal and contain minimal information. Notably, exis…
Pre-trained Large Language Models Use Fourier Features to Compute Addition
Tianyi Zhou, Deqing Fu, Vatsal Sharan +1
Pre-trained large language models (LLMs) exhibit impressive mathematical reasoning capabilities, yet how they compute basic arithmetic, such as addition, remains unclear. This pape…
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
Deqing Fu, Ruohao Guo, Ghazal Khalighinejad +5
Current foundation models exhibit impressive capabilities when prompted either with text only or with both image and text inputs. But do their capabilities change depending on the…
Transformers Learn Low Sensitivity Functions: Investigations and Implications
Bhavya Vasudeva, Deqing Fu, Tianyi Zhou +3
Transformers achieve state-of-the-art accuracy and robustness across many tasks, but an understanding of their inductive biases and how those biases differ from other neural networ…
DeLLMa: Decision Making Under Uncertainty with Large Language Models
Ollie Liu, Deqing Fu, Dani Yogatama +1
The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face chall…