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.

Orca 是一个学习仿真大型模型（LFMs）的推理过程的 130 亿参数模型，通过从 GPT-4 和 ChatGPT 的帮助中获取详细的信号来学习。Orca 在复杂的零 - shot 推理基准测试中的表现超过了 Vicuna-13B 等学习指令调整模型，与 ChatGPT 在 BBH 基准测试中达成并行，并在职业和学术考试中展现出竞争力。

Orca：从 GPT-4 复杂解释跟踪中渐进式学习

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

Solutions to math word problems (MWPs) with step-by-step explanations are
valuable, especially in education, to help students better comprehend
problem-solving strategies. Most existing approaches only focus on obtaining
the final correct answer. A few recent approaches leverage intermediate
solution steps to improve final answer correctness but often cannot generate
coherent steps with a clear solution strategy. Contrary to existing work, we
focus on improving the correctness and coherence of the intermediate solutions
steps. We propose a step-by-step planning approach for intermediate solution
generation, which strategically plans the generation of the next solution step
based on the MWP and the previous solution steps. Our approach first plans the
next step by predicting the necessary math operation needed to proceed, given
history steps, then generates the next step, token-by-token, by prompting a
language model with the predicted math operation. Experiments on the GSM8K
dataset demonstrate that our approach improves the accuracy and
interpretability of the solution on both automatic metrics and human
evaluation.

该研究提出了一种基于逐步规划的中间解决方案生成方法，通过预测所需的数学运算并引导语言模型生成来解决数学单词问题，并在人类评估和自动度量标准上提高了解决方案的准确性和可解释性。