Beyond Behemoths: How Blended Chat AIs Outshine Trillion-Parameters ChatGPT with Elegance

In the realm of conversational Artificial Intelligence (AI) research, the prevailing understanding is that augmenting model parameters and training data size significantly enhances the quality and capability of Large Language Models (LLMs). While the current trend involves scaling up models to staggering sizes, with state-of-the-art systems boasting hundreds of billions of parameters, this approach incurs substantial practical costs in terms of inference overheads. The quest for more compact and efficient chat AIs, capable of retaining user engagement and maintaining conversational quality akin to their larger counterparts, remains imperative.

While a single small model may struggle to rival massive state-of-the-art LLMs, an intriguing question emerges: Can a collective of moderately-sized LLMs collaboratively constitute a chat AI with equivalent or superior abilities? Motivated by this query, a recent paper titled “Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM” by a research team from the University of Cambridge and University College London introduces the Blended approach.