Summary of No Need to Talk: Asynchronous Mixture Of Language Models, by Anastasiia Filippova et al.
No Need to Talk: Asynchronous Mixture of Language Models
by Anastasiia Filippova, Angelos Katharopoulos, David Grangier, Ronan Collobert
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed SmallTalk LM method allows for efficient training of a mixture of language models without requiring high-bandwidth communication between nodes. Each model specializes in distinct parts of the data distribution, and at inference, a lightweight router directs sequences to a single expert based on a short prefix. This approach leverages a fraction of the parameters from the overall mixture model while achieving significantly lower perplexity than dense model baselines for the same training FLOPs and inference cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an innovative way to train language models that are good at different things. Instead of one big model, it trains many smaller models that work together. Each small model is an expert on a specific part of the data. When we want to use these models, we can quickly figure out which expert to ask by looking at just a little bit of the data. This makes the whole process more efficient and accurate. |
Keywords
» Artificial intelligence » Inference » Mixture model » Perplexity
