The advent of Large Language Models (LLMs) has sparked considerable interest in the
medical image domain, as they can generalize to multiple tasks and offer outstanding …

Function calling significantly extends the application boundary of large language models,
where high-quality and diverse training data is critical for unlocking this capability. However …

Instruction Fine-Tuning (IFT) has become an essential method for adapting base Large
Language Models (LLMs) into variants for professional and private use. However …

Building high-quality datasets for specialized tasks is a time-consuming and resource-
intensive process that often requires specialized domain knowledge. We propose Corpus …

Interactive digital agents (IDAs) leverage APIs of stateful digital environments to perform
tasks in response to user requests. While IDAs powered by instruction-tuned large language …

Tool-augmented LLMs are a promising approach to create AI agents that can have realistic
conversations, follow procedures, and call appropriate functions. However, evaluating them …

With recent advances in Large Language Models (LLMs), Agentic AI has become
phenomenal in real-world applications, moving toward multiple LLM-based agents to …

Large language models (LLMs) have facilitated the generation of high-quality, cost-effective
synthetic data for developing downstream models and conducting statistical analyses in …

Image captioning is a critical task at the intersection of computer vision and natural language
processing, with wide-ranging applications across various domains. For complex tasks such …

Evaluations are limited by benchmark availability. As models evolve, there is a need to
create benchmarks that can measure progress on new generative capabilities. However …