# Ten Factors to consider when comparing Large Language Models (LLMs)

Ten Factors to consider when comparing Large Language Models (LLMs) !

{% embed url="https://youtu.be/AuPmHevwIYM" %}

&#x20;The race for AI supremacy has reached a fever pitch. Tech titans like Google, Microsoft, and Meta are locked in an epic battle to build the most powerful large language model (LLM). But with new models emerging at breakneck speed, how do you distinguish the latest GPTs and PaLMs? Join us as we reveal the top 10 secret ingredients for evaluating LLMs. We'll explore everything from training data volume and model architecture to inference efficiency and multimodality. You'll learn all about parameters, context windows, and attention mechanisms. We'll also unpack the role of pre-training, fine-tuning, and prompt engineering. This action-packed deep dive distills the key factors you need to assess any LLM's strengths and weaknesses. Gain the insider knowledge to tell your GPT-3s from your GPT-4s. Get the contextual intelligence to make sense of this rapidly evolving space. Strap in and level up your AI expertise. This is one LLM masterclass you don't want to miss!

&#x20;Keywords:

\#gpt3, #gpt3.5, #chatgpt4, #t5, #gpt4, #webgpt, #gan, #chatgpt, #diffusion, #agi, #asi, #vae, #transformer, #lamda, #llm, #palm, #palm2, #llama, #bloom, #feedforward, #rnn, #cnn, #convolution, #ai #artificialintelligence #deeplearning #neuralnetworks #attention #attentionisallyouneed, #transformerbasedarchitecture, #rlhf



&#x20;
