Orca 2: Small Language Models Can Reason Like the Big Ones

This is a significant development as smaller models have traditionally struggled to perform as well as their larger counterparts at logic and reasoning tasks.

Orca 2 builds on the learnings from Orca 1, which improved the way that smaller-scale language models worked. The premise of the original Orca paper was that if you fine-tune a small model to understand how step-by-step logical reasoning works, then they are going to be good at logical reasoning. This allowed Orca 1 to outperform conventional instruction-tuned models on benchmarks.

In Orca 2, the researchers continued exploring how improved training signals can enhance smaller language models' reasoning abilities. They contend that excessive emphasis on imitation may restrict the potential of smaller models. Instead, they aim to teach smaller models various reasoning techniques and help them determine the most effective solution strategy for each task.

The model was evaluated using a total of fifteen benchmarks covering over 36,000 unique prompts. The benchmarks cover a variety of aspects including language understanding, common sense reasoning, multi-step reasoning, math problem solving, reading comprehension, summarization, contextual relevance, truthfulness, and toxic content generation and identification.

Orca 2's ability to outperform models of comparable size and even those significantly larger is a testament to the advancements in training techniques and model architecture. The results showed that Orca 2, with 13 billion parameters, performed better than any of the other open-source models, even those with 70 billion parameters, in every single benchmark except for one math benchmark. 

This indicates that Orca 2 is not only efficient in terms of size but also effective in its reasoning capabilities.

The success of Orca 2 suggests that the future of language models may not be solely dependent on scaling up the number of parameters. Instead, it points to a future where smarter training techniques and more nuanced model architectures can lead to smaller, more efficient models that do not sacrifice performance. This could lead to a reduction in the computational resources and energy required to run state-of-the-art models, addressing some of the environmental concerns associated with large-scale AI.

Moreover, the ability of Orca 2 to perform complex reasoning tasks opens new possibilities for AI applications. Industries that rely on data analysis, natural language processing, and complex decision-making could benefit significantly from these advancements. For instance, in the legal and financial sectors, where reasoning and logic are paramount, Orca 2-like models could assist in analyzing documents, predicting market trends, or automating complex workflows.

In the broader economic context, the advancements represented by Orca 2 could lead to increased productivity and innovation. As AI becomes more capable and less resource-intensive, businesses of all sizes may integrate AI solutions into their operations, leading to new products, services, and ways of working.

Sources:

https://www.microsoft.com/en-us/research/blog/orca-2-teaching-small-language-models-how-to-reason/