Post
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🧠 How to create more diverse, realistic synthetic AI training data?
@TencentAIGC-Lab AI Lab created @proj-persona , a vast collection of 1 billion diverse personas, to help create synthetic data with LLMs that encapsulate a wide array of perspectives, knowledge, experiences, interests, and professions.
These personas were created with automatically curated data, representing approximately 13% of the world’s total population.
💡 The authors argue that integrating a persona into data synthesis prompts effectively steers LLMs to adopt specific perspectives, creating unique and relevant synthetic data with minimal effort.
They showcased various practical applications of Persona Hub to demonstrate its effectiveness and versatility in various synthetic data creation scenarios: mathematical and logical reasoning problems, simulating diverse user requests and prompts for LLMs, generating informative and detailed text content across various topics, and more.
🚀 It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: "A journalist who covers technology and innovation in the print and digital media industries." It helped generate the prompt attached to this post (about which I'd be curious to know your answers 😉).
Synthetic data is a hot topic in AI. It will be interesting to see if this research could help make LLMs more robust, versatile, and capable of handling a wide array of real-world scenarios.
👉Explore the dataset: proj-persona/PersonaHub
👉 Read the paper: https://arxiv.org/pdf/2406.20094
@TencentAIGC-Lab AI Lab created @proj-persona , a vast collection of 1 billion diverse personas, to help create synthetic data with LLMs that encapsulate a wide array of perspectives, knowledge, experiences, interests, and professions.
These personas were created with automatically curated data, representing approximately 13% of the world’s total population.
💡 The authors argue that integrating a persona into data synthesis prompts effectively steers LLMs to adopt specific perspectives, creating unique and relevant synthetic data with minimal effort.
They showcased various practical applications of Persona Hub to demonstrate its effectiveness and versatility in various synthetic data creation scenarios: mathematical and logical reasoning problems, simulating diverse user requests and prompts for LLMs, generating informative and detailed text content across various topics, and more.
🚀 It's one of the trending datasets on Hugging Face. Digging into it is quite fun! I found one that reminds me of several people I know: "A journalist who covers technology and innovation in the print and digital media industries." It helped generate the prompt attached to this post (about which I'd be curious to know your answers 😉).
Synthetic data is a hot topic in AI. It will be interesting to see if this research could help make LLMs more robust, versatile, and capable of handling a wide array of real-world scenarios.
👉Explore the dataset: proj-persona/PersonaHub
👉 Read the paper: https://arxiv.org/pdf/2406.20094