Given the following transcipt, sumarize what is being said there


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48 hours ago, Microsoft Research released a paper  describing a new language model for code called  
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Phi-1. Despite using few parameters and little  training data by modern standards, Phi-1 gets  
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very strong results. In this video, I'll describe  the key findings of the paper and explain why,  
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together with the Orca work from Microsoft, this  research could represent a step change in the cost  
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of training large language models with significant  implications for the machine learning ecosystem. 
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But first, let's take a step back. To get a handle  on Phi-1, we need to understand the journey of  
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large language models so far. It helps to begin by  looking at what the community has learned over the  
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last few years. Perhaps the best-known empirical  result has been the so-called scaling laws,  
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finding that performance improves somewhat  predictably as one scales up either the  
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amount of compute or the size of the network. In  this work, the authors explore a different axis:  
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the quality of the data. They draw  inspiration from the recent Tiny Stories  
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research on small-scale synthetic data that  highlighted the importance of data quality. 
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The goal of this work is to show that high-quality  data can even improve the state of the art of  
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large language models while dramatically  reducing the dataset size and training  
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compute. This is a result that can significantly  reduce the environmental cost of LLMs. As noted  
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by the authors, I think it may also have  big implications for the ability of teams  
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with fewer resources to build powerful models. The headline results in this paper are reported  
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on HumanEval, which is a dataset of handwritten  problems curated by OpenAI as part of their Codex  
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work, and the Mostly Basic Python Programs  Benchmark of entry-level Python programs  
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introduced by Google. A few things jump out.  First, Phi-1 gets very strong results: 50.6%  
Headline results on HumanEval and MBPP
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on HumanEval, the most widely reported benchmark.  Only Wizard Coder and GPT4 score higher in this  
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table, and Phi-1 outperforms Wizard Coder on MBP. The second is the scale. While Wizard Coder has  
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16 billion parameters and a trillion tokens  of training data, Phi-1 is just 1.3 billion  
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parameters and is trained on 7 billion tokens.  Of course, GPT4 is playing its cards close to its  
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chest. How are these Phi-1 res There should be exactly 3 paragraphs separated by the markdown divider: ***.
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