Google’s Gemma vs Microsoft’s Phi-2 vs Mistral on Summarisation

Author(s): Farhang Dehzad

Originally published on Towards AI.

Image created by author on DALL-E

TL;DR: I’m investigating whether smaller open-source models can provide effective dialogue summarization, a key feature in my medical AI project, Omi. While they don’t have the vast resources of models like GPT-4, these small alternatives could offer specialized, cost-effective solutions for specific tasks like summarising dialogues, which in a clinical setting could potentially save clinicians hours each day.

AI advancements are outpacing Shinkansen bullet-trains. I closely track these developments, especially how open-source models are catching up to giants like OpenAI’s GPT-4. Yet, I remain skeptical that open-source can match the scale of trillion-parameter giants requiring billion-dollar business models. Despite this, open-source models excel in niche areas, using far fewer parameters to reduce energy use and environmental impact. They’ve outperformed GPT-3.5 and sometimes approached GPT-4 levels in specific tasks. This leads me to wonder: How well do these smaller open-source models perform in dialogue summarization?

I’m pursuing the question of summarization because I recently launched Omi: Open Medical Intelligence. My aim with Omi is to develop smaller, highly tailored language models for specific medical use cases. Clinicians face a tedious administrative burden, spending more time behind a computer than with patients. The first use-case I want to tackle… Read the full blog for free on Medium.

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Published via Towards AI