4 papers
Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following
Tianyi Xiong, Yi Ge, Ming Li +13
Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences.…
Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
Qi He, Peiran Yu, Ziyi Chen +1
Shuffling-type gradient methods are favored in practice for their simplicity and rapid empirical performance. Despite extensive development of convergence guarantees under various…
Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training
Reza Shirkavand, Peiran Yu, Qi He +1
Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-…
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
Ruibo Chen, Yihan Wu, Lichang Chen +6
Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a…