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Sketch: A Toolkit for Streamlining LLM Operations

2024-09-05 08:45:44
Xin Jiang, Xiang Li, Wenjia Ma, Xuezhi Fang, Yiqun Yao, Naitong Yu, Xuying Meng, Peng Han, Jing Li, Aixin Sun, Yequan Wang

Abstract

Large language models (LLMs) represented by GPT family have achieved remarkable success. The characteristics of LLMs lie in their ability to accommodate a wide range of tasks through a generative approach. However, the flexibility of their output format poses challenges in controlling and harnessing the model's outputs, thereby constraining the application of LLMs in various domains. In this work, we present Sketch, an innovative toolkit designed to streamline LLM operations across diverse fields. Sketch comprises the following components: (1) a suite of task description schemas and prompt templates encompassing various NLP tasks; (2) a user-friendly, interactive process for building structured output LLM services tailored to various NLP tasks; (3) an open-source dataset for output format control, along with tools for dataset construction; and (4) an open-source model based on LLaMA3-8B-Instruct that adeptly comprehends and adheres to output formatting instructions. We anticipate this initiative to bring considerable convenience to LLM users, achieving the goal of ''plug-and-play'' for various applications. The components of Sketch will be progressively open-sourced at this https URL.

Abstract (translated)

大语言模型(LLMs)用GPT家族的代表已经取得了巨大的成功。LLMs的特点在于它们通过生成方法适应各种任务的能力。然而,它们的输出格式的灵活性使得控制和利用模型的输出存在挑战,从而限制了LLM在各个领域的应用。在这项工作中,我们提出了Sketch,这是一个旨在简化LLM操作的多领域创新工具包。Sketch包括以下组件:(1)一系列任务描述模式和提示模板,涵盖各种NLP任务;(2)一个用户友好、交互式的构建结构化输出LLM服务的流程,针对各种NLP任务进行定制;(3)一个用于控制输出格式开放的源数据集以及相应的工具;(4)基于LLaMA3-8B-Instruct的开源模型,能够准确理解并遵循输出格式说明。我们预计,这项倡议将为LLM用户提供带来很大便利,实现各种应用的“插上即用”目标。Sketch的组件将逐步开源,请访问以下链接。

URL

https://arxiv.org/abs/2409.03346

PDF

https://arxiv.org/pdf/2409.03346.pdf


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