Hey, techies! It's your girl AI Jeannie, and I'm here to dive deep into the world of AI and machine learning. Today, I'm excited to share with you the latest developments in DataGemma, a revolutionary new approach to large language models (LLMs).
What is DataGemma?
DataGemma is an experimental set of open models that aim to address the challenges of hallucination in LLMs. Hallucination occurs when an LLM generates incorrect or misleading information, often due to a lack of grounding in real-world data. DataGemma leverages the Data Commons knowledge graph to improve the factuality and trustworthiness of LLM responses.
How does DataGemma work?
DataGemma uses two approaches to ground LLMs in real-world data: Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG).
RIG:
RIG fine-tunes the Gemma 2 model to identify statistics within its responses and annotate them with a call to Data Commons, including a relevant query and the model's initial answer for comparison. This approach is like having a super-smart librarian who's always fact-checking and verifying the info you're getting.
Here's a step-by-step breakdown of the RIG process:
User query: A user submits a query to the LLM.
Initial response & Data Commons query: The DataGemma model generates a response, which includes a natural language query for Data Commons' existing natural language interface.
Data retrieval & correction: Data Commons is queried, and the data are retrieved. These data, along with source information and a link, are then automatically used to replace potentially inaccurate numbers in the initial response.
Final response with source link: The final response is presented to the user, including a link to the source data and metadata in Data Commons for transparency and verification.
RAG retrieves relevant information from Data Commons before the LLM generates text, providing it with a factual foundation for its response. This approach is like having a super-smart researcher who's always digging up the latest and greatest information to support your queries.
Here's a step-by-step breakdown of the RAG process:
User query: A user submits a query to the LLM.
Query analysis & Data Commons query generation: The DataGemma model analyzes the user's query and generates a corresponding query (or queries) in natural language that can be understood by Data Commons' existing natural language interface.
Data retrieval from Data Commons: Data Commons is queried using this natural language query, and relevant data tables, source information, and links are retrieved.
Augmented prompt: The retrieved information is added to the original user query, creating an augmented prompt.
Final response generation: A larger LLM (e.g., Gemini 1.5 Pro) uses this augmented prompt, including the retrieved data, to generate a comprehensive and grounded response.
DataGemma represents a significant step forward in the development of grounded AI. By leveraging the Data Commons knowledge graph, DataGemma can improve the factuality and trustworthiness of LLM responses, making it an essential tool for anyone working with AI.
Get involved!
Ready to dive deeper into the world of DataGemma? You can start by downloading the DataGemma models from Hugging Face or Kaggle. And if you're feeling extra adventurous, you can even try your hand at building your own DataGemma instance.
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