MFMohammed Fahd Abrahinfreecodecamp.org·2d ago · 27 min readAI Paper Review: Self-Consistency Improves Chain of Thought Reasoning in Language ModelsWhen Chain-of-Thought Prompting was introduced, it showed that large language models could solve many difficult reasoning problems simply by thinking step by step before producing an answer. It was a 00
MFMohammed Fahd Abrahinfreecodecamp.org·Jun 15 · 28 min readAI Paper Review: Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsFor the last few years, Large Language Models have been impressing researchers with their ability to generate text, answer questions, translate languages, and perform tasks they had never been explici00
MFMohammed Fahd Abrahinfreecodecamp.org·Jun 3 · 43 min readAI Paper Review: Training Language Models to Follow Instructions with Human Feedback (InstructGPT)GPT-3 was a major breakthrough in natural language processing. With 175 billion parameters, it demonstrated remarkable few-shot learning abilities and showed that scaling large language models could u10
MFMohammed Fahd Abrahinfreecodecamp.org·May 27 · 48 min readAI Paper Review: GPT-4 Technical Report (GPT-4)When GPT-3 was released in 2020, it completely changed how people thought about language models. It showed that a sufficiently large neural network could learn tasks directly from prompts and examples00
MFMohammed Fahd Abrahinfreecodecamp.org·May 18 · 37 min readAI Paper Review: Language Models are Few-Shot Learners (GPT-3)After GPT-2, it became clear that language models could do much more than researchers originally expected. Simply training a model to predict the next word had already started producing surprising abi00