Weaver also supports retrieval-augmented generation (RAG) and function calling, which can be used to improve AI-assisted writing systems. This includes integrating external knowledge bases, tools, or APIs, and providing personalized writing assistance. The article also discusses guidelines and best practices for pre-training and fine-tuning domain-specific LLMs.
Key takeaways:
- This work introduces Weaver, a family of large language models (LLMs) dedicated to content creation, pre-trained on a carefully selected corpus to improve writing capabilities.
- Weaver models are fine-tuned for creative and professional writing purposes and aligned to the preference of professional writers using novel methods for instruction data synthesis and LLM alignment.
- The Weaver family consists of models of different sizes, suitable for various applications and can be dynamically dispatched by a routing agent according to query complexity to balance response quality and computation cost.
- Weaver natively supports retrieval-augmented generation (RAG) and function calling (tool usage), with various use cases for improving AI-assisted writing systems, including integration of external knowledge bases, tools, or APIs, and providing personalized writing assistance.