Large Language Model
LLM-Pruner: On the Structural Pruning of Large Language Models Xinyin Ma Gongfan Fang Xinchao Wang National University of Singapore
Large language models (LLMs) have shown remarkable capabilities in language understanding and generation.
Seeing the Image: Prioritizing Visual Correlation by Contrastive Alignment
Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner.
the performance of various LLMs on code synthesis. However, these test-cases can be limited in both quantity and quality for fully assessing the functional
Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code.
Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A ".
