Sketch：一个用于简化大语言模型操作的工具包
Sketch: A Toolkit for Streamlining LLM Operations
AI总结：
Sketch工具包通过任务描述模式、交互式结构化输出服务、格式控制数据集和基于LLaMA3-8B-Instruct的格式遵循模型，简化大语言模型在多种NLP任务中的输出控制与即插即用应用。
摘要：
以GPT系列为代表的大语言模型（LLMs）已经取得了显著成功。大语言模型的特点在于其能够通过生成式方法适应广泛的任务。然而，其输出格式的灵活性给控制和驾驭模型输出带来了挑战，从而制约了大语言模型在各个领域的应用。在这项工作中，我们提出了Sketch，一个旨在简化跨领域大语言模型操作的创新工具包。Sketch包含以下组件：（1）一套涵盖各种NLP任务的任务描述模式和提示模板；（2）一个用户友好的交互式流程，用于为各种NLP任务构建结构化输出的大语言模型服务；（3）一个用于输出格式控制的开源数据集，以及用于数据集构建的工具；（4）一个基于LLaMA3-8B-Instruct的开源模型，该模型能够熟练理解并遵循输出格式指令。我们期望这一举措能为大语言模型用户带来极大的便利，实现各种应用的“即插即用”目标。Sketch的组件将逐步在 https://github.com/cofe-ai/Sketch 上开源。
英文摘要：
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 https://github.com/cofe-ai/Sketch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
