Experiments show that training on the high-quality data selected by our method can often outperform other data selection methods for collaborative fine-tuning of LLMs, across diverse private domain datasets, in medical, multilingual and financial settings.

The need to analyze graphs is ubiquitous across various fields, from social networks to biological research and recommendation systems.

However, existing datasets and benchmarks for situated understanding are limited in data modality, diversity, scale, and task scope.

These documents have not been selected following the "most downloaded" principle, as instead is

The comic domain is rapidly advancing with the development of single-page analysis and synthesis models.

Recent advancements in Multimodal Large Language Models (MLLMs) have greatly improved their abilities in image understanding. However, these models often struggle with grasping pixel-level semantic details, e.g., the keypoints of an object. To bridge this gap, we introduce the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypoints across different task scenarios, including keypoint semantic understanding, visual prompt-based keypoint detection, and textual prompt-based keypoint detection.

Thought, the LLM can verbalize that the unknown city is Paris and use this fact to answer downstream questions.

Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast. Our initial run evaluates 22 VLMs on 21 existing datasets to provide a holistic snapshot of the models. We uncover new key findings, such as the fact that efficiency-focused models (e.g., Claude 3 Haiku or Gemini 1.5 Flash) perform significantly

TS also demonstrates strong few-shot and prompt capabilities when applied to new domains and tasks.

Feed Forward Network (FFN) block structure within a standard Transformer model.