Microsoft's Revolutionary AI Model: Orca

Introduction

Microsoft has recently released a groundbreaking research paper on Orca, a revolutionary AI model that learns from complex explanations of gpt4, the most powerful language model in the world. In this blog, we will delve into what Orca is, why it is significant, and how it is poised to transform the field of AI.

Understanding Orca

Orca is a 13 billion parameter model that learns from the complex explanation traces of gpt4. While gpt4 is a much larger model capable of generating a wide range of text, Microsoft's motivation behind creating Orca was to overcome the limitations of bigger models. Larger models are expensive to train and run, require significant computing resources and energy, and are not easily accessible to most researchers and developers.

Smaller models, on the other hand, are more specialized and efficient but tend to have poor reasoning and comprehension skills compared to their larger counterparts. They often make mistakes, provide irrelevant answers, and lack the ability to explain their reasoning process. Orca, however, is not just another smaller model that imitates a bigger model. It learns from the reasoning process of gpt4, using detailed traces of how gpt4 thinks step by step, how it uses logic and common sense, connects different pieces of information, and simplifies complex concepts.

The Importance of Orca

Orca's unique approach sets it apart from other models. By learning from the explanations that gpt4 provides when generating its answers, Orca becomes more capable and intelligent. It excels in handling diverse and challenging tasks, providing accurate and relevant answers, and even explaining its own reasoning process to humans. This breakthrough in open source AI has the potential to empower more people by granting them access to the power of gpt4 without the associated costs and limitations.

Furthermore, Orca opens up new possibilities for AI research and development, particularly in areas that require advanced reasoning and understanding skills.

Understanding gpt4

Before delving deeper into Orca, let's take a closer look at gpt4. Beyond being a text generator, gpt4 excels at reasoning-based tasks such as answering factual questions, summarizing lengthy texts, generating captions, and writing essays. What sets gpt4 apart is its ability to provide explanations for its outputs, which are stored in its internal states or memories. These internal explanations shed light on how gpt4 thinks, solves problems, and uses diverse sources of information.

By leveraging these explanations, Orca gains valuable insights and guidance for performing various tasks and enhancing its reasoning and understanding capabilities. This also makes the learning process more transparent and interpretable for humans.

How Orca Works

Orca is based on Vicuna, a previous open source model fine-tuned on question-answer pairs from GPT 3.5. However, Orca goes a step further by incorporating a technique called explanation tuning. Explanation tuning enhances gpt4's ability to follow specific directives by refining its prompts, such as "summarize this in a sentence" or "explain like I'm a child." This technique makes gpt4's reasoning more transparent.

Orca learns from the explanation traces generated by gpt4 during various tasks, using them as training data to imitate gpt4 as closely as possible. Additionally, Orca generates its own explanations and compares them with gpt4's explanations to improve its performance.

Training Data and Evaluation

To train Orca, it taps into a massive collection called flan 2022, which consists of over 1,000 tasks and 10,000 instructions covering a wide spectrum of subjects. By sampling from this dataset, Orca obtains a variety of tasks to query gpt4 for explanation traces. This diverse training data allows Orca to learn from intricate tasks and fosters various aspects of human intelligence.

Orca's performance is evaluated on several benchmarks, including multiple-choice questions, natural language inference, text summarization, text generation, and image captioning. In most benchmarks, Orca outperforms other open source models of similar or larger size, demonstrating its stellar performance and superior reasoning abilities.

Orca's Versatility and Achievement

Orca's versatility and efficiency are evident across tasks and domains. Despite its smaller size, Orca surpasses larger models like chat GPT in natural language inference and image captioning tasks. Its benchmark performances on Big Bench Hard, Super Glue, CNN Daily Mail, and Coco Captions demonstrate its stellar performance and superiority over other models.

The Significance of Orca

Orca's success highlights several important insights for the future of AI. Firstly, learning from explanations, rather than just answers, significantly boosts AI intelligence and performance. By studying gpt4's explanations, Orca gains superior reasoning skills and provides a transparent look into its problem-solving process.

Secondly, Orca proves that smaller models can match or even outperform larger models. By learning from gpt4, Orca overcomes the limitations of size-related drawbacks and becomes more approachable and efficient, requiring fewer resources and less energy.

Lastly, Orca exemplifies the potential of open source AI and its wider accessibility. By combining gpt4's prowess with open source AI, Orca benefits more people and spurs more applications in the field of AI.

Conclusion

In conclusion, Microsoft's Orca is a game-changing AI model that learns from the complex explanations of gpt4. It addresses the limitations of larger models while still delivering superior performance and reasoning abilities. As an open source model, Orca has the potential to democratize access to powerful AI capabilities and drive further advancements in the field. Orca's success underscores the importance of learning from explanations, the potential of smaller models, and the benefits of open source AI.