Existing evaluation suites tend to be limited, focusing on particular components or specialized use cases.

A straightforward solution is to finetune pre-trained ViTs using high-resolution documents.

By introducing LLM-embedded textual timestamps, Auto-Times can utilize chronological information to align multivariate time series. Empirically, AutoTimes achieves state-of-the-art with 0.1% trainable parameters and

Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection.

Visual grounding is a common vision task that involves grounding descriptive sentences to the corresponding regions of an image. Most existing methods use independent image-text encoding and apply complex hand-crafted modules or encoder-decoder architectures for modal interaction and query reasoning.

We introduce CogVLM, a powerful open-source visual language foundation model.

Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle

Gemma-7B fine-tuned with PiSSA achieves an accuracy of 77.7%, surpassing LoRA's 74.53% by 3.25%. Due to the same architecture, PiSSA is also compatible with quantization to further reduce the memory requirement of fine-tuning.

Recently powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide.

On the other hand, complicated sentences could easily increase the probability of hallucinations.