Large Language Model
Discovering Preference Optimization Algorithms with and for Large Language Models Chris Lu
Typically, preference optimization is approached as an offline supervised learning task using manually crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains under-explored.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation Y uanxin Liu, Lei Li
Recently, open-domain text-to-video (T2V) generation models have made remarkable progress.
Aligning Vision Models with Human Aesthetics in Retrieval: Benchmarks and Algorithms
Modern vision models are trained on very large noisy datasets.
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling.
