5 citations · 6 across the 4 of their papers we have counts for
4 papers
Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training
Yangyi Chen, Hao Peng, Tong Zhang +1
In standard large vision-language models (LVLMs) pre-training, the model typically maximizes the joint probability of the caption conditioned on the image via next-token prediction…
mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules
Carl Edwards, Chi Han, Gawon Lee +11
Despite their ability to understand chemical knowledge, large language models (LLMs) remain limited in their capacity to propose novel molecules with desired functions (e.g., drug-…
Eliminating Position Bias of Language Models: A Mechanistic Approach
Ziqi Wang, Hanlin Zhang, Xiner Li +6
Position bias has proven to be a prevalent issue of modern language models (LMs), where the models prioritize content based on its position within the given context. This bias ofte…
LM-Infinite: Zero-Shot Extreme Length Generalization for Large Language Models
Chi Han, Qifan Wang, Hao Peng +4
Today's large language models (LLMs) typically train on short text segments (e.g., <4K tokens) due to the quadratic complexity of their Transformer architectures. As a result, thei…