New research from FAIR: Better & Faster Large Language Models via Multi-token Prediction. Paper ➡️ https://go.fb.me/o0yd06 We show that replacing next token prediction tasks with multiple token prediction can result in substantially better code generation performance with the exact same training budget and data — while also increasing inference performance by 3x. While similar approaches have previously been used in fine-tuning, this new paper expands to pre-training for large models, showing notable behaviors and results at these scales.
Interesting 🤔, When we train big language models like GPT and Llama, normally they guess one word at a time. But here's the twist: teaching them to guess multiple words at once. By doing this, they learn faster and become better at understanding and generating language.
Reducing the peak GPU utilization from O(nV+d) to O(V+d) is key to making multi-token prediction work. It is interesting to see it improves model performance for larger models (13B) but reduces it for smaller models (0.3B) and increases inference speed 3x.
You need to have a three level architecture for learning in general. The fourth is the customization layer. Data is the key ingredient.
Thanks for sharing. I’m still learning AI development, but this helps a lot
And you can listen to an audio summary of this research paper here: Apple Podcasts: https://podcasts.apple.com/gb/podcast/new-paradigm-ai-research-summaries/id1737607215?i=1000654222601 Spotify: https://open.spotify.com/episode/1gVW9zJL5o843H2cSC8XDg?si=uObcepjaSxiKAPsYcmhW1w This summary is 90% AI generated - I make them for myself to stay on top of fast moving research in the fields of AI and ML. I share them with others to give back to the AI community.
Love to see these "simple" improvements that make a lot of sense. We are only at the beginning of this revolution folks
This is fascinating research! The efficiency gains in both training and inference are impressive and could have significant implications across various industries. At Superposition Mining & Minerals, we're always looking for innovative technologies to enhance our operations. AI-driven advancements in large language models like this could revolutionize our data analysis and predictive maintenance strategies. Looking forward to seeing more breakthroughs from "Given our commitment to leveraging cutting-edge technologies, we're interested in exploring AI solutions tailored for the mining industry. If anyone knows of a sophisticated mineral mining AI platform that could assist with geological analysis, resource estimation, or operational optimization, please share. We're keen to collaborate and integrate such advancements into our processes at Superposition Mining & Minerals in Zambia."
Fellas, we did a deep dive on AlphaSignal 6 days ago :) https://link.alphasignal.ai/lJnZvW
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Software Engineer | Systems | AI/ML | ML Infra | MSCS @ UC Davis
1moThis paper states that multi-token prediction significantly improves performance on coding benchmarks, this could be a breakthrough in developing AI tools for Software Engineering workflows.