Multimodal large language models (MLLM) are advancing rapidly, allowing machines to interpret and reason about textual and visual data simultaneously. ...
LLMs are driving important advances in research and development today. There has been a significant shift in research objectives and ...
Medprompt, a runtime steering strategy, demonstrates the potential to guide general-purpose LLMs to achieve cutting-edge performance in specialized domains such ...
Large Language Models (LLMs) have revolutionized characteristic dialect preparing (NLP), fueling applications extending from summarization and interpretation to conversational operators ...
Neural contextual bias allows speech recognition models to leverage contextually relevant information, improving transcription accuracy. However, the bias mechanism is ...
Retrieval-Augmented Generation (RAG) is a framework that improves language models by combining two main components: Retriever and Generator. A RAG ...
Budgets for educational technology continue to be a challenge, especially when many schools' finances are stretched thin to begin with. ...
Large language models (LLMs) have demonstrated consistent scaling laws, revealing a power-law relationship between pre-training performance and computational resources. This ...
Training large language models (LLMs) that can handle long context processing remains a difficult task due to limitations of data ...
Researchers have focused on developing and building models to efficiently process and compare human language in natural language processing. A ...
