Limitations in either capability can impede the overall performance of a VLM. A systematic evaluation of the perception and reasoning capabilities is crucial to provide valuable insights for future model optimization.

Given that warmup is standard in the practitioner's toolkit, it is important to understand it deeply and

"Universalization"-based reasoning, a theory of moral thinking, are able to achieve

Recent work demonstrates that, post-training large language models with open-domain instruction following data have achieved colossal success.

Evaluating machine learning models requires evaluation data.

Overcoming this challenge, existing object hallucination evaluation methods average the results obtained from a set of instructions. However, these methods fail to provide consistent evaluation across instruction sets that generate image descriptions of significantly different lengths.

We demonstrate FlexCap's effectiveness in several applications: first, it achieves strong performance in dense captioning tasks on the Visual Genome dataset. Second, we show how FlexCap's localized

The croissant metadata record is available at croissant. We chose GitHub and Google Drive respectively to store our code and dataset. Both are widely recognized as reliable data storage platforms, ensuring long-term preservation. We highly recommend downloading the raw data directly and following the provided instructions to simplify the data processing steps. Our dataset is structured as follows: the local directory contains client-specific data for local training, while all clients aggregates data from all clients for federated learning.

Based on FedLLM-Bench, we conduct experiments on all datasets to benchmark existing FL methods and provide empirical insights (e.g., multilingual collaboration). We believe that our FedLLM-Bench can benefit the FedLLM community by reducing required efforts, providing a practical testbed, and promoting fair comparisons.