Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM).We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-ofthe-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned stateof-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies. * Equal Contribution. Author contributions and ordering details are listed in Appendix A.

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Effective scaling and a flexible task interface enable large language models to excel at many tasks. PaLI (Pathways Language and Image model) extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pretrained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train the largest ViT to date (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-ofthe-art in multiple vision and language tasks (such as captioning, visual questionanswering, scene-text understanding), while retaining a simple, modular, and scalable design.

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Background: Necrotizing fasciitis is a limb or life-threatening, invasive soft-tissue infection, although known since ancient times was first described in 1871. The purpose of the present study is to analyze the clinical presentation, bacterial flora, predisposing and co-morbid conditions, surgical and resuscitative treatment and outcome of this soft tissue infection.Methods: This is a prospective, cross-sectional, clinical study which was conducted from May 2016 until April 2018. This study included 60 consecutive patients. Age, gender, clinical presentation, co-morbid conditions and predisposing factors and pus culture sensitivity, treatment given, and its outcome were recorded.Results: With a male female ratio of 2.33:1 and median age of 46, most involves site was lower limb. Most patients presented with history of trauma followed by swelling (100%), pain (91.67%), and pus discharge (48.33%). Group A streptococcus was the most common organism isolated in both monomicrobial (53.85%) and polymicrobial (63.83%) cultures. Diabetes was the most common co morbidity observed. Fasciotomy and debridement were the mainstay of treatment with amputation rate of 23.33% and a mortality of 25%.Conclusions: Timely surgical intervention in the form of fasciotomy and debridement along with correction of malnutrition and anemia can be crucial in saving lives and limbs.