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Sthenno

sthenno
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https://github.com/neoheartbeats

AI & ML interests

To contact me: [email protected]

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reacted to MoritzLaurer's post with ❀️ 1 day ago
FACTS is a great paper from @GoogleDeepMind on measuring the factuality of LLM outputs. You can now download their prompt templates from @huggingface to improve LLM-based fact-checking yourself! πŸ“ The paper introduces the FACTS Grounding benchmark for evaluating the factuality of LLM outputs. πŸ€– Fact-checking is automated by an ensemble of LLM judges that verify if a response is fully grounded in a factual reference document. πŸ§ͺ The authors tested different prompt templates on held-out data to ensure their generalization. πŸ“š It's highly educational to read these templates to learn how frontier labs design prompts and understand their limitations. πŸ’Ύ You can now download and reuse these prompt templates via the prompt-templates library! πŸ”„ The library simplifies sharing prompt templates on the HF hub or locally via standardized YAML files. Let’s make LLM work more transparent and reproducible by sharing more templates like this! Links πŸ‘‡ - prompt-templates docs: https://moritzlaurer.github.io/prompt_templates/ - all templates on the HF Hub: https://huggingface.co/datasets/MoritzLaurer/facts-grounding-prompts - FACTS paper: https://storage.googleapis.com/deepmind-media/FACTS/FACTS_grounding_paper.pdf
reacted to CultriX's post with ❀️ 1 day ago
# Space for Multi-Agent Workflows using AutoGen Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows. By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements. In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow. # Workflow Steps 1. User Input: - The user defines a prompt, such as "Write a random password generator using python." - Outcome: A clear task for the primary assistant to accomplish. 2. Primary Assistant Work: - The primary assistant begins working on the provided prompt. It generates an initial code snippet based on the user's request. - Outcome: An initial proposal for the requested code. 3. Critic Feedback: - The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message. (This process repeats until the output is APPROVED or 10 messages have been exchanged). - Outcome: A revised Python function that incorporates the critic's feedback. 4. Documentation Generation: - Once the code is approved, it is passed to a documentation assistant. The documentation assistant generates a concise documentation for the final code. - Outcome: A short documentation including function description, parameters, and return values. Enjoy! https://huggingface.co/spaces/CultriX/AutoGen-MultiAgent-Example
liked a dataset 1 day ago
DAMO-NLP-SG/multimodal_textbook
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