Impressive progress has been made on chat models based on Large Language
Models (LLMs) recently; however, there is a noticeable lag in multi-turn
conversations between open-source chat models (e.g., Alpaca and Vicuna) and the
leading chat models (e.g., ChatGPT and GPT-4). Through a series of analyses, we
attribute the lag to the lack of enough high-quality multi-turn
instruction-tuning data. The available instruction-tuning data for the
community are either single-turn conversations or multi-turn ones with certain
issues, such as non-human-like instructions, less detailed responses, or rare
topic shifts. In this paper, we address these challenges by introducing Parrot,
a highly scalable solution designed to automatically generate high-quality
instruction-tuning data, which are then used to enhance the effectiveness of
chat models in multi-turn conversations. Specifically, we start by training the
Parrot-Ask model, which is designed to emulate real users in generating
instructions. We then utilize Parrot-Ask to engage in multi-turn conversations
with ChatGPT across a diverse range of topics, resulting in a collection of 40K
high-quality multi-turn dialogues (Parrot-40K). These data are subsequently
employed to train a chat model that we have named Parrot-Chat. We demonstrate
that the dialogues gathered from Parrot-Ask markedly outperform existing
multi-turn instruction-following datasets in critical metrics, including topic
diversity, number of turns, and resemblance to human conversation. With only
40K training examples, Parrot-Chat achieves strong performance against other
13B open-source models across a range of instruction-following benchmarks, and
particularly excels in evaluations of multi-turn capabilities. We make all
codes, datasets, and two versions of the Parrot-Ask model based on LLaMA2-13B
and KuaiYii-13B available at this https URL

通过引入 Parrot，一种具有高可扩展性的解决方案，我们在多轮对话中自动生成高质量的指导语料，并利用这些语料来提高聊天模型在多轮对话中的效果。