Researchers have developed a method called Magpie that synthesizes alignment data for large language models (LLMs) without using instruction prompts. This approach, demonstrated through Llama-3-Instruct, shows the ability to decode user queries effectively. A demo showcasing Magpie's data generation process has also been created.
I created a quick @gradio demo for Magpie, a really interesting method for generating data from an existing LLM without relying on instruction prompts. It's fascinating to see what user instructions an LLM produces without prompting! Check it out here: https://t.co/szShzNXk1B. https://t.co/jHT2UAFlWc
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing https://t.co/LDdeUsToEb
What if we prompt aligned LLMs like Llama-3-Instruct with nothing? 🤔Surprisingly, it will decode decent user queries thanks to its auto-regressive nature. In our new preprint, Magpie🐦⬛, we find this is a scalable way to self-synthesize instruction data of high quality &… https://t.co/AZqG4OXfKP
Magpie Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain https://t.co/ye4wTwdJXe
This is the full video of the talk I gave earlier today about LLM usage on the command-line I put together a handout to accompany the talk here: https://t.co/xYJoAiJzFE https://t.co/ea89iP5Z97