XLM vs GPT-4

Discover the power of Cross-lingual Language Modeling with XLM, the original PyTorch implementation designed for pretraining language models that operate across multiple languages. XLM is a repository that provides the foundation for creating multilingual NLP systems using techniques such as Masked Language Modeling (MLM) and Translation Language Modeling (TLM). These methods facilitate the learning of language representations that generalize well to various linguistic tasks, enabling better understanding and processing of text in a variety of languages.

With XLM, researchers and developers can harness the benefits of transfer learning in the context of Natural Language Processing (NLP) for languages with limited training data, by leveraging existing data from high-resource languages. The repository was archived on October 31, 2023, and is now in a read-only state, making it an essential resource for historical reference and study in the field of cross-lingual language model pretraining.

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.