The PersonaHub adapted from “Scaling Synthetic Data Creation with 1,
000,000,000 Personas”.PersonaHub proposes a novel persona-driven data synthesis methodology
that leverages various perspectives within a large language model (LLM) to
create diverse synthetic data. By showcasing PersonaHub’s use cases in
synthesizing high-quality mathematical and logical reasoning problems,
instructions (i.e., user prompts), knowledge-rich texts, game NPCs and
tools (functions) at scale, the authors demonstrate persona-driven data
synthesis is versatile, scalable, flexible, and easy to use, potentially
driving a paradigm shift in synthetic data creation and applications in
practice, which may have a profound impact on LLM research and development.
Please refer to the paper for more details: https://arxiv.org/pdf/2406.20094.Parameters:
model (BaseModelBackend, optional): The model to use for persona generation and manipulation. (default: :obj:None)