Beware the Orca, the challenge to ChatGPT and Palm2 is here

 

So Google's 'we have no moat' paper was correct. If you train an LLM wisely then it's cost effective and cheap to produce a small LLM that is able to compete or even beat established, costly LLMs, as Microsoft has just found. It's another excellent video from AI Explained, who goes through some of the training procedures, which I won't get into here.

Orca, is a model that learns from large foundation models (LFMs) like GPT-4 and ChatGPT by imitating their reasoning process. Orca uses rich signals such as explanations and complex instructions to improve its performance on various tasks. Orca outperforms other instruction-tuned models and achieves similar results to ChatGPT on zero-shot reasoning benchmarks and professional and academic exams. The paper suggests that learning from explanations is a promising way to enhance model skills.

• Smaller models are often overestimated in their abilities compared to LFMs, and need more rigorous evaluation methods.
• Explanation tuning is a technique that helps smaller models imitate the reasoning process of LFMs by using rich signals such as explanations and complex instructions.
• Data size and coverage, as well as the quality of the base model, are important factors that affect the performance of smaller models.
• Orca is a state-of-the-art smaller model that can match or surpass ChatGPT in some tasks, but still lags behind GPT-4. This shows that smaller models can be trained to be more focused and adaptable, but also need to learn from step-by-step explanations.

The paper also suggests some directions for future research and development in this field, such as improving evaluation methods, alignment and post-training techniques, and using both GPT-4, and Chat -GPT 3,5 as a teacher. Orca was best when subjected to both models in turn of complexity, pure GPT-4 proved too complex and less performative. 

It would seem, that through some further careful fine tuning, Orca could be nearly as performant, for some tasks, if not most, as GPT-4. Argilla might be an excellent option for this fine tuning. 'Argilla Feedback is completely open-source and the first of its kind at the enterprise level. With its unique focus on scalable human feedback collection, Argilla Feedback is designed to boost the performance and safety of Large Language Models (LLMs).'Utilised with Tree of Thoughts as an input model, linked to a API app store, such as Gorilla, then the challenge presented by Orca would be significant enough to make any corporation think twice about the value of their existing commercial investments with established LLM providers. Especially when such a model can run on a high end home computer, rather than a super computer with its associated costs.