Why are Large Language Models general learners? 64 HN points • 12 Jun 23 🕹 Technology AI Machine Learning Language Models Predicting the next token well requires understanding the underlying reality. Training large language models on next token prediction tasks leads to general learning abilities. A deeper understanding of reality boosts performance in predicting the next token in various tasks.
How much better can Large Language Models get? 19 implied HN points • 23 Aug 23 🕹 Technology AI Machine Learning Forecasting Training Large Language Models requires significant compute and financial investment. Improvements in Large Language Models come from scaling up models, data, and compute in tandem. Understanding scaling laws can help forecast the future performance of Large Language Models.
The Fundamental Quantities of LLMs: Part Two - 🖥️ Compute 4 HN points • 28 May 23 🕹 Technology AI Hardware Computing Training Inference LLMs require a massive amount of compute to be trained due to billions of parameters. Compute is measured in FLOPs (floating point operations per second) to quantify the work computers do. GPUs, born out of video games, play a crucial role in handling the immense compute demands of training large language models.
The Fundamental Quantities of LLMs: Part Three - 📈 Model Performance 1 HN point • 14 Jul 23 🕹 Technology Models Performance Analysis Evaluation Comparison The open-source large language model Vicuna-13B challenged ChatGPT in performance Model IQ measures general large language model performance Specific capability metrics measure skills like logical reasoning or medical knowledge
The Four Fundamental Quantities of LLMs: Part Zero - 📜 What are Large Language Models? 1 HN point • 21 May 23 🕹 Technology Machine Learning Neural Networks Training Data Large Language Models Large language models (LLMs) are neural networks with billions of parameters trained to predict the next word using large amounts of text data. LLMs use parameters learned during training to make predictions based on input data during the inference stage. Training an LLM involves optimizing the model to predict the next token in a sentence by feeding it billions of sentences to adjust its parameters.
Abrupt skill emergence in Large Language Models 0 implied HN points • 31 Aug 23 🕹 Technology AI Machine Learning Data science Computing Research General Large Language Model performance can be predicted based on compute, dataset size, and parameter count. Task-specific abilities in models show abrupt jumps in proficiency as the parameter count increases. Abrupt skill emergence is observed in models for tasks like adding numbers or unscrambling words as they reach certain parameter thresholds.