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Gio Paik, Geewook Kim, Jinbae Im. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

While large language models (LLMs) have integrated images, adapting them to graphs remains challenging, limiting their applications in materials and drug...

Abstract page for arXiv paper 2401.13919v1: WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models

We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new capabilities of perceiving object descriptions (e.g., bounding boxes) and...

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Recent advancements in multimodal large language models (MLLMs) have enhanced document understanding by integrating textual and visual information. However,...

Learn how to optimize multimodal model inference with batch-level data parallelism for vision encoders in vLLM, achieving up to 45% throughput gains on AMD...

In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad...

Humans extract useful abstractions of the world from noisy sensory data. Serial reproduction allows us to study how people construe the world through a...

Training models on synthetic data is an effective strategy for improving large multimodal models (LMMs) due to the scarcity of high-quality paired image-text...