New AI Approach Using Embedding Recycling (ER) Offers 2x Faster Training and 1.8x Speedup In Inference for Language Model Development


Are you looking for a way to speed up the training and inference of language models? If so, you’re in luck! A revolutionary new approach called Embedding Recycling (ER) has just been introduced and it promises to revolutionize language model development. In this blog post, we’ll discuss what Embedding Recycling is and how it can help improve the efficiency of language models.

Let’s start by talking about what language models are. Language models are one of the best advancements in Artificial Intelligence. They are trained on massive amounts of textual data and are used for a variety of tasks, including summarization of articles, writing stories, answering questions, and completing codes. The latest development by OpenAI, GPT-3, already has millions of users and 175 billion parameters.

Now that we understand what language models are, let’s talk about how they work. Language models work with the help of several computational layers, including the input layer, embedding layer, hidden layers, and output layer. The weights in a model portray the strength of the networks between the neurons, which determines the performance of the model and the correctness of the output.

This is where Embedding Recycling comes in. Embedding Recycling is a new approach that can improve efficiency and reuse the sequence representations from the preceding model runs. It retains the sequence representations during training and saves time and resources when several language models run over the same corpus of textual data.

The research team consisting of AI2, Yale and Northwestern researchers, have tested this technique for 14 different tasks and eight language models. It showed an increase in the training speed by 90% and 87 to 91% speedup in the inference. All this has been achieved with only a minimal loss in the F-1 metric.

Embedding Recycling is unquestionably a great method for reducing the computational costs of training and inference. It introduces layer recycling with the help of fine-tuning and parameter-efficient adapters, which seems favorable for the efficient usage of language models. Consequently, Embedding Reying is an amazing breakthrough in language model development.

So, if you’re looking for a way to speed up the training and inference of language models, Embedding Recycling is the way to go. Check out the paper, Github and reference article for more information.

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