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Retrieval Augmented NLP

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State-of-the-art language models (LMs) are now so large that only a handful of labs can afford to train them. The scale is driven by the training setup which forces the LM to memorise as much factual information as possible. However, humans already have a tool for finding information: search engines. We will discuss the recent trend of extending LMs with an information retrieval component, which allows order of magnitude smaller models to outcompete even the largest LMs. We will specifically focus on the most recent developments: LaMDA, WebGPT, and RETRO . Finally, we will touch on the design of modern search engines, and draw parallels to retrieval augmented LMs.

Recommended reading:

  1. RETRO : https://arxiv.org/abs/2112.04426
  2. WebGPT: https://arxiv.org/abs/2112.09332
  3. LaMDA: https://arxiv.org/abs/2201.08239

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This talk is part of the Machine Learning Reading Group @ CUED series.

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