Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2409.03346 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929487651799040 |
|---|---|
| author | Jiang, Xin Li, Xiang Ma, Wenjia Fang, Xuezhi Yao, Yiqun Yu, Naitong Meng, Xuying Han, Peng Li, Jing Sun, Aixin Wang, Yequan |
| author_facet | Jiang, Xin Li, Xiang Ma, Wenjia Fang, Xuezhi Yao, Yiqun Yu, Naitong Meng, Xuying Han, Peng Li, Jing Sun, Aixin Wang, Yequan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03346 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sketch: A Toolkit for Streamlining LLM Operations Jiang, Xin Li, Xiang Ma, Wenjia Fang, Xuezhi Yao, Yiqun Yu, Naitong Meng, Xuying Han, Peng Li, Jing Sun, Aixin Wang, Yequan Computation and Language Artificial Intelligence 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. |
| title | Sketch: A Toolkit for Streamlining LLM Operations |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.03346 |