Microsoft has introduced a new open-source LLM named Orca 13B. This 13-billion parameter model is intended to compete with other large language models like GPT-4. Here are some key questions and answers about this:
What is Orca 13B?
Orca 13B is a large language model developed by Microsoft. It’s designed to learn from and compete with other large language models like GPT-4. It has 13 billion parameters and is (soon to be) open-source, making it accessible to the wider AI community.
How is Orca 13B different from GPT-4?
The main difference between Orca 13B and GPT-4 is in the way they learn and reason. Orca 13B learns from complex explanation traces and step-by-step thought processes from GPT-4. This allows Orca 13B to have a better understanding of the reasoning process, which helps it provide more detailed and accurate responses. This learning approach significantly enhances Orca 13B’s reasoning and comprehension skills compared to GPT-4.
What kind of tasks is Orca 13B trained on?
To enhance Orca’s learning process, the research team uses the Flan 2022 Collection. The team samples tasks from this extensive collection to ensure a diverse mix of challenges. These tasks are then sub-sampled to generate complex prompts, which serve as queries for large foundation models. This approach creates a diverse and rich training set for Orca 13B.
How does Orca 13B perform compared to other models?
Evaluations have shown that Orca 13B outperforms state-of-the-art instruction-tuned models like Vicuna-13B, showing an improvement of over 100% on BigBench Hard (BBH). Orca 13B also exhibits competitive performance on academic exams in zero-shot settings, indicating its potential for real-world applications.
What is the significance of Orca 13B’s development?
The introduction of Orca 13B represents a significant breakthrough in advancing instruction-tuned models. By learning from step-by-step explanations and scaling tasks with complex prompts, Orca 13B achieves significant advancements in instruction-tuned models. This holds promise for fully unlocking the potential of large language models and driving progress in natural language processing.
Read more: Full paper
(By Assaf Yablon)
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