UL2 vs GPT-4

The research paper titled "UL2: Unifying Language Learning Paradigms" focuses on creating a comprehensive framework for pre-training language models that excel across various datasets and setups, confronting the challenge that existing pre-trained models are often specialized for specific types of problems. The authors, Yi Tay, and team, have disentangled architectural archetypes from pre-training objectives to present a broadened self-supervision perspective within NLP. A novel pre-training objective named Mixture-of-Denoisers (MoD) is introduced, blending different pre-training approaches. Additionally, the paper explores mode switching, which ties downstream fine-tuning to definite pre-training methods.

Through rigorous experimentation, the authors demonstrate that their method, especially when scaled up to 20B parameters, gains state-of-the-art (SOTA) accolades on 50 known NLP tasks and showcases impressive in-context learning capabilities, outshining models like GPT-3 and T5 in various benchmarks. The team has publicly released Flax-based T5X checkpoints for their UL2 20B & Flan-UL2 20B models, a significant contribution for NLP research and application.

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.