Textual Model

Textual models aim to understand, generate, and manipulate text data, encompassing diverse tasks from text detection in images to question answering and knowledge graph completion. Current research emphasizes improving model fidelity and robustness through techniques like contextual grounding, leveraging large language models (LLMs) and transformer architectures, and exploring multimodal integration with speech and visual data. These advancements are crucial for enhancing applications across various fields, including finance, healthcare, and scientific research, by enabling more accurate, reliable, and interpretable text processing.