Weaver：創意寫作的基礎模型

Weaver: Foundation Models for Creative Writing

January 30, 2024

作者: Tiannan Wang, Jiamin Chen, Qingrui Jia, Shuai Wang, Ruoyu Fang, Huilin Wang, Zhaowei Gao, Chunzhao Xie, Chuou Xu, Jihong Dai, Yibin Liu, Jialong Wu, Shengwei Ding, Long Li, Zhiwei Huang, Xinle Deng, Teng Yu, Gangan Ma, Han Xiao, Zixin Chen, Danjun Xiang, Yunxia Wang, Yuanyuan Zhu, Yi Xiao, Jing Wang, Yiru Wang, Siran Ding, Jiayang Huang, Jiayi Xu, Yilihamu Tayier, Zhenyu Hu, Yuan Gao, Chengfeng Zheng, Yueshu Ye, Yihang Li, Lei Wan, Xinyue Jiang, Yujie Wang, Siyu Cheng, Zhule Song, Xiangru Tang, Xiaohua Xu, Ningyu Zhang, Huajun Chen, Yuchen Eleanor Jiang, Wangchunshu Zhou

cs.AI

摘要

本研究介紹了Weaver，我們首個專注於內容創作的大型語言模型（LLMs）家族。Weaver在一個精心挑選的語料庫上進行預訓練，專注於提升大型語言模型的寫作能力。然後，我們對Weaver進行微調，用於創意和專業寫作，並通過一套新穎的方法進行指導數據合成和LLM對齊，使其能夠生成更接近人類的文本，並遵循更多樣化的內容創作指令。Weaver家族包括Weaver Mini（1.8B）、Weaver Base（6B）、Weaver Pro（14B）和Weaver Ultra（34B）等不同尺寸的模型，適用於不同應用，並可根據查詢複雜度由路由代理動態調度，以平衡響應質量和計算成本。在一個精心策劃的基準測試中評估LLMs的寫作能力，顯示Weaver各尺寸的模型在性能上優於比它們大數倍的通用LLMs。值得注意的是，我們最強大的Weaver Ultra模型在各種寫作場景中超越了GPT-4，一個最先進的通用LLM，展示了專門為寫作目的訓練LLMs的優勢。此外，Weaver原生支持檢索增強生成（RAG）和函數調用（工具使用）。我們展示了這些能力的各種用例，用於改進AI輔助寫作系統，包括整合外部知識庫、工具或API，以及提供個性化的寫作輔助。此外，我們討論並總結了領域特定LLMs的預訓練和微調的指南和最佳實踐。

English

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.