Insane New AI Model - ORCA - That Finally Beats GPT-4

 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 in this video so 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 almost 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's 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 enerating captions and so on these smaller models are usually fine-tuned on specific data sets or instructions to make them more specialized and efficient 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 answers when faced with complex or ambiguous queries they also lack the ability to explain how they arrived at their answers or what steps they took to solve a 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 a bigger model it learns from the explanations that gpt4 gives when it generates its answers these explanations are 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 how it simplifies complex concepts by learning from these explanations Orca  becomes much more capable and

intelligent than other models it can handle more diverse and challenging tasks it can give more accurate and relevant answers and it can also explain its own reasoning process to humans this is a huge breakthrough for open source AI Orca is set to be open source soon

which means anyone will be able to use it and build upon it it will enable more people to access the power of gpt4 without having to pay for it or deal with its limitationsOrca  will also open up new possibilities for AI research and development especially in areas that require more reasoning and understanding skills 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 and 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 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 summarize this in a sentence or create a love Haiku we make it more Adept at particular tasks but explanation tuning goes beyond it hones gpt4 to reveal its thought process using prompts like think sequentially or explain like I'm a child this way gpt4's reasoning 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 with the standard prompt who leads France and the explanation prompt think in steps gpt4 might 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 with 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 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 13bs 30 and surpassing chat gpts 59 and gpt4s 62 on super glue SG it achieves an 86 average beating vicuna 13B 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 text DaVinci 003 39 chat GPT 40 and closing in on GPT 4 42 on Coco captions CC it scores a cider of 120 higher than vicuna 13B 113 text DaVinci 0.003 115 chat GPT 117 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 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 steps size 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 spur more applications 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 open source 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 ai's accessibility and demonstrating the potential of explanation-based learning alright that's it for this video thank

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