Pathways Language Model (PaLM) vs GPT-4

The Pathways Language Model (PaLM) represents a significant technological advancement in the field of language models, scaling up to an unprecedented 540 billion parameters. Coming from the prestigious Google Research Blog, this model sets new benchmarks in the domain of natural language understanding and generation through few-shot learning – a method that bypasses extensive data collection and model tuning for specific tasks. Developed using Google's Pathways system, PaLM leverages dense decoder-only Transformer architecture across multiple TPU v4 Pods, exemplifying efficient distributed computation. Notable are its impressive performance improvements across a broad spectral range of tasks, including reasoning and code generation, as well as PaLM's capabilities in multiple languages and its ethical considerations. Google's PaLM aims to serve as a foundational step towards creating AI systems that can generalize and understand multimodal data efficiently.

GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning.

GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

GPT-4 is more creative and collaborative than ever before. It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.