![]() PaLM demonstrates impressive natural language understanding and generation capabilities on several BIG-bench tasks. Scaling behavior of PaLM on a subset of 58 BIG-bench tasks. ![]() PaLM 540B 5-shot also does better than the average performance of people asked to solve the same tasks. Interestingly, we note that PaLM’s performance as a function of scale follows a log-linear behavior similar to prior models, suggesting that performance improvements from scale have not yet plateaued. We compare the performance of PaLM to Gopher and Chinchilla, averaged across a common subset of 58 of these tasks. We also probe emerging and future capabilities of PaLM on the Beyond the Imitation Game Benchmark (BIG-bench), a recently released suite of more than 150 new language modeling tasks, and find that PaLM achieves breakthrough performance. In addition to English NLP tasks, PaLM also shows strong performance on multilingual NLP benchmarks, including translation, even though only 22% of the training corpus is non-English. PaLM 540B performance improvement over prior state-of-the-art (SOTA) results on 29 English-based NLP tasks. PaLM 540B surpassed few-shot performance of prior large models, such as GLaM, GPT-3, Megatron-Turing NLG, Gopher, Chinchilla, and LaMDA, on 28 of 29 of tasks that span question-answering tasks (open-domain closed-book variant), cloze and sentence-completion tasks, Winograd-style tasks, in-context reading comprehension tasks, common-sense reasoning tasks, SuperGLUE tasks, and natural language inference tasks. We evaluated PaLM on 29 widely-used English natural language processing (NLP) tasks. We 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. PaLM was trained using a combination of English and multilingual datasets that include high-quality web documents, books, Wikipedia, conversations, and GitHub code. This is due 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 optimizations. PaLM achieves a training efficiency of 57.8% hardware FLOPs utilization, the highest yet achieved for LLMs at this scale. This is a significant increase in scale compared to most previous LLMs, which were either trained on a single TPU v3 Pod (e.g., GLaM, LaMDA), used pipeline parallelism to scale to 2240 A100 GPUs across GPU clusters ( Megatron-Turing NLG) or used multiple TPU v3 Pods ( Gopher) with a maximum scale of 4096 TPU v3 chips. The training is scaled using data parallelism at the Pod level across two Cloud TPU v4 Pods, while using standard data and model parallelism within each Pod. PaLM demonstrates the first large-scale use of the Pathways system to scale training to 6144 chips, the largest TPU-based system configuration used for training to date. Training a 540-Billion Parameter Language Model with Pathways We evaluated PaLM on hundreds of language understanding and generation tasks, and found that it achieves state-of-the-art few-shot performance across most tasks, by significant margins in many cases.Īs the scale of the model increases, the performance improves across tasks while also unlocking new capabilities. In “ PaLM: Scaling Language Modeling with Pathways”, we introduce the Pathways Language Model (PaLM), a 540-billion parameter, dense decoder-only Transformer model trained with the Pathways system, which enabled us to efficiently train a single model across multiple TPU v4 Pods. An important milestone toward realizing this vision was to develop the new Pathways system to orchestrate distributed computation for accelerators. Last year Google Research announced our vision for Pathways, a single model that could generalize across domains and tasks while being highly efficient. Yet much work remains in understanding the capabilities that emerge with few-shot learning as we push the limits of model scale. More recent LLMs, such as GLaM, LaMDA, Gopher, and Megatron-Turing NLG, achieved state-of-the-art few-shot results on many tasks by scaling model size, using sparsely activated modules, and training on larger datasets from more diverse sources. GPT-3 first showed that large language models (LLMs) can be used for few-shot learning and can achieve impressive results without large-scale task-specific data collection or model parameter updating. In recent years, large neural networks trained for language understanding and generation have achieved impressive results across a wide range of tasks. Posted by Sharan Narang and Aakanksha Chowdhery, Software Engineers, Google Research
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