[2204.02311] PaLM: Scaling Language Modeling with Pathways #897
Labels
base-model
llm base models not finetuned for chat
code-generation
code generation models and tools like copilot and aider
human-verified
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in-context-learning
Examples of few-shot prompts for in-context learning.
llm
Large Language Models
llm-evaluation
Evaluating Large Language Models performance and behavior through human-written evaluation sets
MachineLearning
ML Models, Training and Inference
ml-inference
Running and serving ML models.
Models
LLM and ML model repos and links
Papers
Research papers
Research
personal research notes for a topic
PaLM: Scaling Language Modeling with Pathways
Snippet
"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-of-the-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 state-of-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."
Subjects
Computation and Language (cs.CL)
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