Google AI had introduced the Pathways Language Model (PaLM), a 540-billion parameter, dense decoder-only Transformer model trained with the Pathways system used to train a single model across multiple TPU v4 Pods. The researchers evaluated PaLM on hundreds of language understanding and generation tasks and achieved state-of-the-art few-shot performance across most tasks, by significant margins in many cases.
PaLM achieves a training efficiency of 57.8% hardware FLOPs utilisation, the highest yet achieved for LLMs at this scale, thanks to a combination of the parallelism strategy and a reformulation of the Transformer block that allows for attention and feedforward layers to be computed in parallel, enabling speedups from TPU compiler optimisations.
PaLM was trained using a combination of English and multilingual datasets that include high-quality web documents, books, Wikipedia, conversations, and GitHub code. The researchers also created a “lossless” vocabulary that preserves all whitespace (especially important for code), splits out-of-vocabulary Unicode characters into bytes, and splits numbers into individual tokens, one for each digit.
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PaLM showed breakthrough capabilities on numerous difficult tasks. When tested against other language models, PaLM 540B surpassed few-shot performance on language understanding and generation when evaluated on 29 widely-used English natural language processing (NLP) tasks. In addition, PaLM demonstrated impressive natural language understanding and generation capabilities on several BIG-bench tasks.
PaLM exhibited breakthrough capabilities on reasoning tasks that require multi-step arithmetic or common-sense reasoning. Prior LLMs, like Gopher, saw less benefit from model scale in improving performance.