Author: Ricardo

The Challenge of Updating LLM Knowledge LLMs have shown outstanding performance for various tasks through extensive pre-training on vast datasets. However, these models frequently generate outdated or inaccurate information and can reflect biases during deployment, so their knowledge needs to be updated continuously. Traditional fine-tuning methods are expensive and susceptible to catastrophic forgetting. This has…

The Need for Efficient On-Device Language Models Large language models have become integral to AI systems, enabling tasks like multilingual translation, virtual assistance, and automated reasoning through transformer-based architectures. While highly capable, these models are typically large, requiring powerful cloud infrastructure for training and inference. This reliance leads to latency, high costs, and privacy concerns,…