Enhancing Large Language Models with Diverse Instruction Data: A Clustering and Iterative Refinement Approach
Enhancing Large Language Models with Diverse Instruction Data: A Clustering and Iterative Refinement Approach Large language models (LLMs) have become a pivotal part of artificial intelligence, enabling systems to understand, generate, and respond to human language. These models are used across various domains, including natural language reasoning, code generation, and problem-solving. LLMs are usually trained on vast amounts of unstructured data from the internet, allowing them to develop broad […] The post Enhancing Large Language Models with Diverse Instruction Data: A Clustering and Iterative Refinement Approach appeared first on MarkTechPost . Summary The article discusses advancements in enhancing large language models (LLMs) by utilizing diverse instruction data through a clustering and iterative refinement approach. LLMs play a crucial role in artificial intelligence by enabling systems to understand and generate human language. They are extensively applied in areas such as n