Biology needs to be reduced to fundamental components so that mysticism and religion do not corrupt its might.
Same happens to LLMs. These are not thinking machines, in any definition of thinking we may agree on. And the NTP reduction clearly shows that.

Mostly agree here, though I would rather use the word mimic than persuade. Persuade entails a purpose, which I'm not sure LLMs have. That, is, does a mathematical function have a purpose?

Exactly! The most effective control measure, RAG, is still a patch that can provide no technical guarantee. Just a strong bias that models may not follow.

The sooner we understand the limits of LLMs, the sooner we'll learn to deploy them properly.

Though both data sources have the same origin (visual inspection of embryo change) I'd expect features found by humans and features found by a neural net to be complementary.

I guess the intrinsic variance is what dominates here. We can only know so much about an embryo by just looking at it.

The recipe is simple 🧑‍🍳 :
1. A good open model 🍞
2. A properly tuned RAG pipeline 🍒

And you will be cooking a five star AI system ⭐ ⭐ ⭐ ⭐ ⭐

See you on the AIAI 2025 conference, where we will be presenting this work, done at @bsc-cns.bsky.social and @hpai.bsky.social

Disclaimer: Only text questions were used to evaluate LLMs, unlike students. Student's score computed under the assumption that all questions were answered, which may not be the case.
buff.ly/3Xa9gFc

I like the cell one 👍

I'm an empiricist, so we attack metrics by developing adversarial benchmarks that expose model shortcuts. Plus, its a lot of fun to show how fragile LLMs can be.

While writing a paper I consistently learn general insights that are too general or not tested enough to be sold as paper contributions, but are great for conversation :)

Remarkable effort. Questionable motivation.