Although large language models (LLMs) have advanced the state-of-the-art in
NLP significantly, deploying them for downstream applications is still
challenging due to cost, responsiveness, control, or concerns around privacy
and security. As such, trainable models are still the preferred option in some
cases. However, these models still require human-labeled data for optimal
performance, which is expensive and time-consuming to obtain. In order to
address this issue, several techniques to reduce human effort involve labeling
or generating data using LLMs. Although these methods are effective for certain
applications, in practice they encounter difficulties in real-world scenarios.
Labeling data requires careful data selection, while generating data
necessitates task-specific prompt engineering. In this paper, we propose a
unified data creation pipeline that requires only a single formatting example,
and which is applicable to a broad range of tasks, including traditionally
problematic ones with semantically devoid label spaces. In our experiments we
demonstrate that instruction-following LLMs are highly cost-effective data
creators, and that models trained with these data exhibit performance better
than those trained with human-labeled data (by up to 17.5%) on
out-of-distribution evaluation, while maintaining comparable performance on
in-distribution tasks. These results have important implications for the
robustness of NLP systems deployed in the real-world.

本文提出了一种统一的数据创建流程，只需一个格式示例，适用于包括传统上问题较多的任务在内的广泛范围，通过实验证明使用指令跟随型大型语言模型创建的数据比使用人工标注的数据在分布外评估上表现更好（高达 17.5%），同时在分布内任务上保持可比较的性能，这些结果对于在现实世界中部署的自然语言处理系统的稳健性具有重要意义。

提升大型语言模型的数据生成能力

Making Large Language Models Better Data Creators

At the beginning era of large language model, it is quite critical to
generate a high-quality financial dataset to fine-tune a large language model
for financial related tasks. Thus, this paper presents a carefully designed
data creation pipeline for this purpose. Particularly, we initiate a dialogue
between an AI investor and financial expert using ChatGPT and incorporate the
feedback of human financial experts, leading to the refinement of the dataset.
This pipeline yielded a robust instruction tuning dataset comprised of 103k
multi-turn chats. Extensive experiments have been conducted on this dataset to
evaluate the model's performance by adopting an external GPT-4 as the judge.
The promising experimental results verify that our approach led to significant
advancements in generating accurate, relevant, and financial-style responses
from AI models, and thus providing a powerful tool for applications within the
financial sector.

通过创造一个数据创建流程，将大规模语言模型 fine-tune 为金融相关任务，本研究使用 ChatGPT 在 AI 投资者和金融专家之间开展对话，并结合金融专家的反馈，生成了一个包含 103k 条对话的稳健指令调整数据集。通过采用 GPT-4 作为评判模型，在该数据集上进行了广泛实验，结果表明我们的方法在从 AI 模型中生成准确、相关和金融风格的回答方面取得了重大进展，为金融领域的应用提供了有力工具。