UniLM vs Gopher

UniLM is a pre-trained language model from Microsoft Research that handles both natural language understanding and text generation from one shared Transformer. You fine-tune a single checkpoint for reading tasks like question answering and writing tasks like summarization or dialogue, without maintaining separate encoder-only and decoder-only models. Code and pretrained weights ship through the microsoft/unilm GitHub repo under an MIT license.

BERT-style models excel at reading but need a separate decoder stack for generation. UniLM trains one Transformer with three attention-mask modes: unidirectional, bidirectional, and sequence-to-sequence. That design let the same weights compete with BERT on GLUE and SQuAD while setting summarization and question-generation benchmarks in 2019, a split that most contemporaries treated as two problems.

ML researchers and NLP engineers use UniLM when they want published benchmarks, training scripts, and checkpoint files for both understanding and generation in one codebase. The repository now spans later releases like UniLMv2 (Pseudo-Masked Language Model, ICML 2020), but v1 remains the reference for the original unified masking approach described in the NeurIPS 2019 paper.

Discover the cutting-edge advancements in artificial intelligence with DeepMind's exploration of language processing capabilities in AI. At the heart of this exploration is Gopher, a 280-billion-parameter language model designed to understand and generate human-like text. Language serves as the core of human intelligence, enabling us to express thoughts, create memories, and foster understanding.

Realizing its importance, DeepMind's interdisciplinary teams have endeavored to drive the development of language models like Gopher, balancing innovation with ethical considerations and safety. Learn how these language models are advancing AI research by enhancing performance in tasks ranging from reading comprehension to fact-checking while identifying limitations such as logical reasoning challenges. Attention is also given to the potential ethical and social risks associated with large language models, including the propagation of biases and misinformation, and the steps being taken to mitigate these risks.