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It was nicknamed Strawberry because it's an organic model, not a static GPT. What's buried in the straw, is their new jam. 🍎

I missed the live presentation, but this is just as good. Thanks for this incredible material. I am passionate about the disinformation and misinformation in the age of deluge of information, coupled with powerful tools at the disposal of everyday folks. It matters how these tools are deployed and how the unsuspecting vulnerable majority are protected from the potential harm. Thanks Cherie and Team!

It was nothing about the Finance.

Subscribed

I was looking for a good summary around LLM evaluation metrics.. I see a lot of them captured here well

💖 'Promo SM'

Who is Diego M. Oppenheimer?

Interesting

Awesome talk! So much cool info

Fantastic presentation, Max and Rowan! The depth of your analysis and the clarity with which you presented the complexities of evaluating LLMs is truly commendable. It's evident that a lot of thought and effort went into this research. I'm particularly intrigued by your approach to using LLMs as evaluators. It opens up a plethora of possibilities but also brings forth some ethical considerations. How do you account for systemic biases in evaluation metrics when using LLMs as evaluators? Given that traditional metrics might not capture the fairness aspect adequately, have you considered incorporating fairness metrics or mitigation methods in your evaluation process?

Excellent overview, Terry. The part about identifying age discrimination within machine learning models caught my attention. Could you share more about how Arthur.AI's platform sets the acceptable range for performance metrics in this context? Is it customizable based on industry or legal standards?

Any way to boost the audio on this? Barely audible in some parts

That's a resourceful conversation, folks. Thanks for hosting.

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