Weaver: Foundation Models for Creative Writing
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914659758505984 |
|---|---|
| author | Wang, Tiannan Chen, Jiamin Jia, Qingrui Wang, Shuai Fang, Ruoyu Wang, Huilin Gao, Zhaowei Xie, Chunzhao Xu, Chuou Dai, Jihong Liu, Yibin Wu, Jialong Ding, Shengwei Li, Long Huang, Zhiwei Deng, Xinle Yu, Teng Ma, Gangan Xiao, Han Chen, Zixin Xiang, Danjun Wang, Yunxia Zhu, Yuanyuan Xiao, Yi Wang, Jing Wang, Yiru Ding, Siran Huang, Jiayang Xu, Jiayi Tayier, Yilihamu Hu, Zhenyu Gao, Yuan Zheng, Chengfeng Ye, Yueshu Li, Yihang Wan, Lei Jiang, Xinyue Wang, Yujie Cheng, Siyu Song, Zhule Tang, Xiangru Xu, Xiaohua Zhang, Ningyu Chen, Huajun Jiang, Yuchen Eleanor Zhou, Wangchunshu |
| author_facet | Wang, Tiannan Chen, Jiamin Jia, Qingrui Wang, Shuai Fang, Ruoyu Wang, Huilin Gao, Zhaowei Xie, Chunzhao Xu, Chuou Dai, Jihong Liu, Yibin Wu, Jialong Ding, Shengwei Li, Long Huang, Zhiwei Deng, Xinle Yu, Teng Ma, Gangan Xiao, Han Chen, Zixin Xiang, Danjun Wang, Yunxia Zhu, Yuanyuan Xiao, Yi Wang, Jing Wang, Yiru Ding, Siran Huang, Jiayang Xu, Jiayi Tayier, Yilihamu Hu, Zhenyu Gao, Yuan Zheng, Chengfeng Ye, Yueshu Li, Yihang Wan, Lei Jiang, Xinyue Wang, Yujie Cheng, Siyu Song, Zhule Tang, Xiangru Xu, Xiaohua Zhang, Ningyu Chen, Huajun Jiang, Yuchen Eleanor Zhou, Wangchunshu |
| contents | This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suit of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17268 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weaver: Foundation Models for Creative Writing Wang, Tiannan Chen, Jiamin Jia, Qingrui Wang, Shuai Fang, Ruoyu Wang, Huilin Gao, Zhaowei Xie, Chunzhao Xu, Chuou Dai, Jihong Liu, Yibin Wu, Jialong Ding, Shengwei Li, Long Huang, Zhiwei Deng, Xinle Yu, Teng Ma, Gangan Xiao, Han Chen, Zixin Xiang, Danjun Wang, Yunxia Zhu, Yuanyuan Xiao, Yi Wang, Jing Wang, Yiru Ding, Siran Huang, Jiayang Xu, Jiayi Tayier, Yilihamu Hu, Zhenyu Gao, Yuan Zheng, Chengfeng Ye, Yueshu Li, Yihang Wan, Lei Jiang, Xinyue Wang, Yujie Cheng, Siyu Song, Zhule Tang, Xiangru Xu, Xiaohua Zhang, Ningyu Chen, Huajun Jiang, Yuchen Eleanor Zhou, Wangchunshu Computation and Language Artificial Intelligence Machine Learning This work introduces Weaver, our first family of large language models (LLMs) dedicated to content creation. Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models. We then fine-tune Weaver for creative and professional writing purposes and align it to the preference of professional writers using a suit of novel methods for instruction data synthesis and LLM alignment, making it able to produce more human-like texts and follow more diverse instructions for content creation. The Weaver family consists of models of Weaver Mini (1.8B), Weaver Base (6B), Weaver Pro (14B), and Weaver Ultra (34B) sizes, suitable for different applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost. Evaluation on a carefully curated benchmark for assessing the writing capabilities of LLMs shows Weaver models of all sizes outperform generalist LLMs several times larger than them. Notably, our most-capable Weaver Ultra model surpasses GPT-4, a state-of-the-art generalist LLM, on various writing scenarios, demonstrating the advantage of training specialized LLMs for writing purposes. Moreover, Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage). We present various use cases of these abilities for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance. Furthermore, we discuss and summarize a guideline and best practices for pre-training and fine-tuning domain-specific LLMs. |
| title | Weaver: Foundation Models for Creative Writing |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.17268 |