Orca 2 🐳 GIANT Innovation For AI Logic/Reasoning

1. Orca 2 improves the reasoning abilities of smaller language models.

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Orca 2 research paper demonstrates that smaller language models can perform just as well as larger models in logic and reasoning tasks.

• Orca 2 builds on the learnings from Orca 1 and introduces improved training signals.
• The goal of Orca 2 is to help models determine the most effective solution strategy for each task.

2. Orca 2 teaches smaller models various reasoning techniques.

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Orca 2 teaches smaller models step-by-step processing, recall, reasoning, extraction, and direct answer methods.

• These techniques enhance the reasoning abilities of smaller models.
• Orca 2 carefully tailors the reasoning strategies to the task at hand.

3. Orca 2 uses prompt eraser technique to improve reasoning.

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Prompt eraser technique involves exposing smaller models only to the task and the resultant behavior without showing them the part of the prompt that instructed the larger model.

• This technique helps smaller models reason without relying on mimicking larger models.
• Orca 2 selects behaviors from larger models that are best suited for the task at hand.

4. Orca 2 surpasses models of similar size in reasoning tasks.

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Orca 2 outperforms models 5 to 10 times larger in complex tasks that test advanced reasoning abilities.

• Orca 2 achieves performance levels similar or better than larger models.
• It performs well in zero-shot settings, where it is not nudged or given hints.

5. Instruction tuning and explanation tuning enhance small model performance.

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Instruction tuning improves the model's ability to follow instructions and generate high-quality output.

• Explanation tuning helps small models reason more carefully.
• Both techniques enhance zero-shot and reasoning capabilities.

6. Orca 2 carefully selects the right reasoning strategy for each task.

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Orca 2 tailors the reasoning strategies to the specific task, allowing models to perform at their best.

• Not every task can be solved by the same reasoning strategy.
• Orca 2 uses a reservoir of behaviors from larger models to select the best strategy.

7. Orca 2 evaluation compares its performance to other models.

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Orca 2 is evaluated against several other models on 15 benchmarks covering various aspects of language understanding, reasoning, math problem solving, and more.

• Orca 2 significantly surpasses models of similar size in performance.
• It demonstrates strong reasoning abilities and outperforms other open source models.

8. The strategy an LLM uses to reason about a task should depend on the task itself.

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The optimal strategy for a smaller model may differ from that of a more powerful one.

• The actual tool being used might be different for smaller models compared to larger models.
• Smaller models might require a step-by-step approach instead of generating a direct answer.

9. Orca 2 has been trained with Progressive learning.

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The training process of Orca 2 has shown a relative improvement of 47% over Lama 2 and 28% over Wizard LM 13B.

• Orca 2 outperforms larger models like Lama 2 and performs comparably to Wizard LM 70B.
• Orca 2 has been trained on a new dataset with 87,000 training instances.

10. Orca 2 performs well on reasoning benchmarks.

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Orca 2 performs 25% better than Lama Chat 13B and 44% better than Wizard LM 13B on average.

• Orca 2 surpasses 70B baselines and performs comparably with 13B models.
• Orca 2 performs well on benchmarks like AGI eval, DROP, CRaSS, GSM 8K, and more.

11. Improving the reasoning capabilities of smaller language models is attainable.

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Orca 2 demonstrates that smaller language models can be improved through training on tailored synthetic data.

• Orca 2 shows that smaller models can achieve notable performance in logic and reasoning tasks.
• Training on synthetic data helps address limitations of smaller language models.

12. Logic and reasoning tests are recommended.

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Consider taking logic and reasoning tests to improve your cognitive abilities.

• These tests can help enhance critical thinking and problem-solving skills.
• They are often used in job interviews and academic settings.