If you consider language fashions in relation to generative artificial intelligence (AI), the primary time period that in all probability involves thoughts is giant language mannequin (LLM). These LLMs energy hottest chatbots, similar to ChatGPT, Bard, and Copilot. Nevertheless, Microsoft’s new language mannequin is right here to indicate that small language fashions (SLMs) have nice promise within the generative AI house, too.
On Wednesday, Microsoft released Phi-2, a small language mannequin able to common sense reasoning and language understanding, and it is now out there within the Azure AI Studio mannequin catalog.
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Do not let the phrase small idiot you, although. Phi-2 packs 2.7 billion parameters in its mannequin, which is a giant bounce from Phi-1.5, which had 1.3 billion parameters.
Regardless of its compactness, Phi-2 showcased “state-of-the-art efficiency” amongst language fashions with lower than 13 billion parameters, and it even outperformed fashions as much as 25 instances bigger on advanced benchmarks, in keeping with Microsoft.
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Phi-2 outperformed fashions — together with Meta’s Llama-2, Mistral, and even Google’s Gemini Nano 2, which is the smallest model of Google’s most succesful LLM, Gemini — on a number of completely different benchmarks, as seen beneath.
Phi-2’s efficiency outcomes are congruent with Microsoft’s aim with Phi of creating an SLM with emergent capabilities and efficiency akin to fashions on a a lot bigger scale.
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“A query stays whether or not such emergent talents could be achieved at a smaller scale utilizing strategic decisions for coaching, e.g., knowledge choice,” stated Microsoft.
“Our line of labor with the Phi fashions goals to reply this query by coaching SLMs that obtain efficiency on par with fashions of a lot bigger scale (but nonetheless removed from the frontier fashions).”
When coaching Phi-2, Microsoft was very selective concerning the knowledge used. The corporate first used what it calls “text-book high quality” knowledge. Microsoft then augmented the language mannequin database by including rigorously chosen net knowledge, which was filtered on instructional worth and content material high quality.
So, why is Microsoft centered on SLMs?
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SLMs are a cheap various to LLMs. Smaller fashions are additionally helpful when they’re getting used for for a job that is not demanding sufficient to require the facility of an LLM.
Moreover, the computational energy required to run SLMs is way lower than LLMs. This decreased requirement means customers do not essentially must spend money on costly GPUs to energy their data-processing necessities.