# Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Source: https://arxiv.org/abs/2306.02707
Kiwi News: https://news.kiwistand.com/stories/Orca-Progressive-Learning-from-Complex-Explanation-Traces-of-GPT-4?index=0x648858578a425c6affa6863c338432d435e32d297b86d9a9dbe6961e6e83a6562cdee7f7
Submitted by m-j-r.eth on 2023-06-13 UTC, 1 upvote, 0 comments

## Summary

Orca, a 13-billion parameter model developed by Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi and Ahmed Awadallah, learns to imitate the reasoning process of large foundation models. It is trained on explanation traces and step-by-step thought processes from GPT-4, guided by teacher assistance from ChatGPT. The authors report that Orca surpasses Vicuna-13B by more than 100% on Big-Bench Hard and by 42% on AGIEval in zero-shot reasoning, reaches parity with ChatGPT on BBH, and trails GPT-4 on professional and academic exams such as the SAT, LSAT, GRE and GMAT.

## Article

Orca: Progressive Learning from Complex Explanation Traces of GPT-4

Authors: Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah

Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number of issues impact the quality of these models, ranging from limited imitation signals from shallow LFM outputs; small scale homogeneous training data; and most notably a lack of rigorous evaluation resulting in overestimating the small model's capability as they tend to learn to imitate the style, but not the reasoning process of LFMs. To address these challenges, we develop Orca (We are working with our legal team to publicly release a diff of the model weights in accordance with LLaMA's release policy to be published at this https URL), a 13-billion parameter model that learns to imitate the reasoning process of LFMs. Orca learns from rich signals from GPT-4 including explanation traces; step-by-step thought processes; and other complex instructions, guided by teacher assistance from ChatGPT. To promote this progressive learning, we tap into large-scale and diverse imitation data with judicious sampling and selection. Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance (4 pts gap with optimized system message) in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot settings without CoT; while trailing behind GPT-4. Our research indicates that learning from step-by-step explanations, whether these are generated by humans or more advanced AI models, is a promising direction to improve model capabilities and skills.