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  • GitHub - nyanp chat2plot: chat to visualization with LLM
    Inside Chat2Plot, LLM does not generate Python code, but generates plot specifications in json The declarative visualization specification in json is transformed into actual charts in Chat2Plot using plotly or altair, but users can also use json directly in their own applications
  • POWER BI: Values appear in Transform Data but not in the visualization
    You can try activate and deactivate the different relations that you have (one by one) and see if the data missing appears suddenly If this happens, the problem might be related with this column you have last deactivate
  • How to visualise LLMs ? : r LocalLLaMA - Reddit
    The output of these models is indeed generated by predicting the next token based on the previous ones However, it's important to note that this prediction doesn't involve the model having an 'assumption' or an 'intelligence', rather it's based on the patterns it learned during training
  • Chat2VIS: AI-driven visualisations with Streamlit and natural language
    Chat2VIS is an app that generates data visualisations via natural language using GPT-3, ChatGPT-3 5, and GPT-4 LLMs You can ask it to visualise anything from movies to cars to clothes, to even energy production Let me show how it works by using a fun example Have you heard of speedcubing?
  • How to Generate Visualizations with Large Language Models (ChatGPT, GPT4)
    Issue #12 | How to build tools for automatic data exploration, grammar-agnostic visualizations and infographics using Large Language Models like ChatGPT and GPT4
  • How to Get Better Outputs from Your Large Language Model
    If a prompt is not working out, try changing the way that you structured it Consider certain phrases Often when you want your model to answer your prompts logically and arrive at accurate conclusions or simply to make the model achieve a certain outcome, you can consider using the following phrases:
  • Chat with your CSV: Visualize Your Data with Langchain and Streamlit
    In this article, I will show how to use Langchain to analyze CSV files We will use the OpenAI API to access GPT-3, and Streamlit to create a user interface The user will be able to upload a CSV file and ask questions about the data The system will then generate answers, and it can also draw tables and graphs
  • LLM Prompting: How to Prompt LLMs for Best Results
    ‍The key metrics you can test your LLM prompts for include: Grounding — Grounding is determined by comparing the LLM’s outputs against ground truths in a specific domain This metric can show you how accurate your LLM is in specific domain knowledge


















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