Are larger models actually better? New research challenges this notion! A paper released in January 2024 titled "Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM" questions the "bigger is better" approach in AI language models. 🔑 Key findings: • Researchers introduced "Blending" - a method to combine multiple smaller chat AI models • A blend of three 6-13B parameter models outperformed ChatGPT (175B+ parameters) in user engagement and retention • Blended approach offers comparable or better performance at a fraction of the computational cost ❓Why it matters: • Potential for more efficient and accessible AI systems • Demonstrates the power of model collaboration over simply scaling up parameters • Could reshape how we approach building large language models 🌿 Environmental and Industry Impact: • The race for larger models has intensified competition for AI chips, straining global supply chains • "Blending" could offer a more sustainable path forward, reducing energy consumption and hardware needs 💼 Implications for Startups and New Entrants: • This approach could level the playing field in AI development • Startups and smaller companies can potentially compete with tech giants • Innovative approaches show that breakthrough performance doesn't always require massive investments This research opens exciting possibilities for creating more engaging AI conversations without the computational demands of trillion-parameter models. What are your thoughts? Could blending smaller models be the future of conversational AI? How might this impact the AI startup ecosystem? Paper linked in comment 💬 #AI #MachineLearning #NLP #Sustainability #TechInnovation #AIStartups https://lnkd.in/dtz68VwB
Well put!
Software Engineer / Digital Analyst at McKinsey and Company | ML / NLP Engineer
2wBlending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM paper 📄: https://arxiv.org/pdf/2401.02994