Mark Huang (co-founder of Gradient.ai) discusses long-context LLM evaluation metrics on the Latent Space podcast with Swix.
Insight
Huang: True AI agents require measurable probability improvements per node
“It's like on each stage of the node, you're gonna have to see a marginal improvement in the probability of success for that particular workload because of non-determinism.”
Opinion
Huang: Google's internal AI tooling was far superior to competitors
“Google was using AI for systems before everybody else too, right? They invented a transformer, and their internal set of tooling was just so far superior to everything else. Like, it's really hard for people to go back after seeing that.”
Insight
Huang: RAG versus fine-tuning is fundamentally just meta-learning
“And like, at the end of the day, it's just all meta-learning, right? Like, all we want is, like, the best meta learning workflow or meta learning setup possible to be able to adapt the model to do anything.”
Assertion Open · timeframe May 2025
Huang: PoSE breaks down on needle-in-a-haystack at 500k tokens
“It does start to break down a little bit more on the longer, longer context. So, like, 500,000 to a million it appeared that it doesn't hold as well specifically for, like, needle in the haystack.”
Insight
Huang: Adding one billion tokens cannot teach trillion-token models new knowledge
“All models these days are now double-digit trillions, right? So it's kind of a drop in the bucket if you really think I can just put, you know, a billion tokens in there, and I actually think that the model's gonna truly learn new Information.”
Assertion Supported
Huang: Training CodeLlama on Llama 2 caused catastrophic language forgetting
“We do have historical precedent where CodeLlama was, you know, trained further from the original CodeLlama was trained further from Lama II, and it just lost, All its language capabilities, basically, right?”