Microsoft AI Introduces Orca: A 13-Billion Parameter Model

Microsoft has released a new research paper on Orca a revolutionary AI model that learns from complex explanations of gpt4 the most powerful language model in the world this is a huge deal and I’m going to tell you why,

what is orca and why is it so important

well, Orca is a 13 billion parameter model that learns from complex explanation traces of gpt4 which is a much bigger model that can generate most any kind of text you can imagine now why would Microsoft want to create a smaller model that learns from a bigger model isn’t bigger always better when it comes to AI well not exactly bigger models are more powerful but they also have some drawbacks they are very expensive to train and run they require a lot of computing resources and energy and they are not very accessible to most researchers and developers that are why there has been a lot of interest in creating smaller models that can still perform well on various tasks such as answering questions, summarizing texts generating captions and so on these smaller models are usually fine-tuned on specific data sets or instructions to make them more specialized and efficient.

The Limitations of Smaller Models

However there is a problem with this approach smaller models tend to have poor reasoning and comprehension skills compared to bigger models they often make mistakes or give irrelevant answerswhen faced with complex or ambiguous queries they also lack the ability toexplain how they arrived at their answers or what steps they took to solvea problem but Orca is not just another smaller model that imitates a bigger model Orca is a smaller model that learns from the reasoning process of abigger model it learns from the explanations that gpt4 gives when it generates its answers these explanationsare not just simple sentences or phrases they are detailed traces of how gpt4 thinks step by step how it uses logic and Common Sense how it connects Different pieces of information and howit simplifies complex concepts bylearning from these explanations Orca becomes much more capable andintelligent than other models it can handle more diverse and challenging tasks it can give more accurate andrelevant answers and it can also explainits own reasoning process to humans thisis a huge breakthrough for open source AI Orca is set to be open source soon which means anyone will be able to useit and build upon it it will enable morepeople to access the power of gpt4 without having to pay for it or deal with its limitations. Orca will also open up new possibilities for AI research and development, especially in areas that require more reasoning and understanding skills.

Understanding the Inner Workings of Orca

To understand how Orca works we need to First understand how gpt4 works so gpt4 is more than a text generator it performs tasks requiring reasoning like answering factual questions summarizing lengthy texts generating captions writing essays more interestingly gpt4 can provide explanations for its outputs these are found in the model’s internal States essentially its thoughts or memories which hold the logic and information used to generate outputs by using specific prompts we can unveil these internal explanations giving a detailed view of how gpt4 thinks solves problems and uses diverse sources of information including its own memory the web and Common Sense. 

These explanations are very valuable for smaller models that want to learn from gpt4 they provide more signals and guidance for how to perform various tasks and how to reason and understand different concepts They also make the learning process more transparent and interpretable for humans this is what Orca does.

Leveraging Explanations: Orca’s Learning Process

Orca learns from these explanations that gpt4 generates when it performs different tasks it uses these explanations as its training data and tries to imitate them as closely as possible Orca also tries to generate its own explanations when it performs similar tasks and Compares them with gpt4’s explanations to improve itself so Orca is actually based on vicuna a previous open source model that was fine-tuned on question-answer pairs from GPT 3.5. Orca extends by kuna by adding a new technique called explanation tuning which allows it to learn from complex explanation traces of gpt4.

Explanation tuning is a Fresh Approach that enhances gpt4’s skill to follow specific directives by refining this AI with prompts like summarizing this in a sentence or creating a love Haiku we make it more Adept at particular tasks but explanation tuning goes beyond it honesgpt4 to reveal its thought process using prompts like think sequentially or explain like I’m a child this way gpt4’sreasoning becomes more transparent this technique involves standard and explanation prompts former our usual tasks like who leads France or craft a winter poem the latter instruct gpt4 to clarify its logic like think in steps or show how you did it using both prompt types together gpt4 produces complex explanation traces.

for instance, the standard prompt Who leads France and the explanation prompt think in steps gpt4might provide a step-by-step explanation this comprehensive response not only tells us who the president is but also illustrates gpt4’s problem-solving strategy and information sources offering more insight than a simple answer.

Orca leverages explanation traces as learning material striving to mimic them and generate its own for improvement but where do these traces come from Orca Taps into Flan 2022 a massive collection of over 1 000 tasks and 10 000 instructions covering a spectrum of subjects by sampling from flan 2022 Orca gets a variety of tasks and uses them to query gpt4 for explanation traces it also creates complex prompts from the data set to test gpt4’s reasoning like mashing two tasks into one this way Orca learns from diverse and intricate tasks fostering many aspects of human intelligence,

Orca is evaluated on a number of benchmarks that test its generative reasoning and comprehension abilities these benchmarks include multiple choice questions, natural language inference text summarization, text Generation, image captioning, and so on.

Orca is compared to other models of similar size or larger size such as Vikuna 13B, text DaVinci 003 a free version of gpt3 chat, GPT 3.5, and gpt4 orca’s performance is Stellar topping all other open source models in most benchmarks particularly those needing deeper reasoning despite its smaller size it matches or beats chat GPT in many areas even competing with gpt4 in tasks like natural language inference or image captioning. 

Benchmark Performance: Orca’s Superiority

Here’s a quick look at Orca’s Benchmark performances

on big bench hard BBH it scores a 64% accuracy more than double of Vicuna’s 13bs 30% and surpasses chatgpts59% and gpt4 62%

on super glue  it achieves an 86% average beating vicuna13B 81%, Tex DaVinci 003 83% chat GPT 84%  and nearly matching GPT 4 88%,

on CNN daily mail CDM Orca earns a rugel score of 41% outperforming Vicuna 13B 38% textDaVinci 003 39% chat GPT 40% and closing in on GPT4  42%

on Coco captions CC it scores a cider of 120% higher than vicuna13B 113 %, text DaVinci 0.003 115%, chat GPT117%, and GPT 4 119%

so as you can see Orca is a highly versatile efficient model performing well across tasks and domains and soon to be open source it also works on a single GPU.

Insights into the Future of AI

orca’s success reveals multiple insights about ai’s future firstly it indicates that learning from explanations as opposed to just answers notably boosts AI intelligence and performance by studying gpt4’s explanations Orca not only gains Superior reasoning skills but also provides a transparent look into its problem-solving process secondly Orca proves that despite their size smaller models can match or outperform larger ones learning from gpt4 Orca side stepsize related drawbacks showing that smaller models can be more approachable and efficient needing fewer resources and energy and thirdly orca exemplifies how open source AI through inventive methods can match proprietary Ai and demonstrates how open source ai’s wider accessibility can benefit more people and support more applications.

Orca’s Unique Position

concerning its positioning Orca isn’t just a mini gpt4 or another open source model while it doesn’t match gpt4’s broad capacity or knowledge base it harnesses gpt4’s reasoning making it smarter than other small models it also surpasses gpt4 and transparency by generating its own explanation traces unlike other opensource models Orca learns from a varied range of tasks and complex explanations making it more intelligent and versatile therefore Orca occupies a unique position in the AI sphere combining gpt4’s prowess with open source inaccessibility and demonstrating the potential of explanation-based learning 

Alright that’s it for this article thank you so much for reading