But, it seems unreasonable to be super worried about this -- a year or two ago, models couldn't easily find needles in haystacks of long context. As training and test strategies delivered trainable content, this became a thing that could be done perfectly across millions of tokens of context. There has not been a good way to incentivize models to do anything more but remember locations yet.
We are (mostly) paying the full costs of attending to the entire context in current architectures, and it seems pretty reasonable that we will therefore be able to train those architectures to more fully attend across context if we get the right training data into (ideally) an RL loop.
NoLima is an okay test, but I think the most recent OpenAI tests are significantly better and quite interesting; OpenAI-MRCR and Graphwalks are both super smart ideas about how to programmatically generate data that is easy to evaluate and forces better cross context attention.
From their 4.1 announcement: Graphwalks fills the context window with a directed graph composed of hexadecimal hashes, and then asks the model to perform a breadth-first search (BFS) starting from a random node in the graph. We then ask it to return all nodes at a certain depth.
MRCR asks for direct quotes at semantically identified locations in the text, e.g. poems about tapirs, bears and ballerinas, as well as stories about tapirs, bears and ballerinas are generated, perhaps fifty each. The system is asked "give me the third poem about tapirs". This requires counting, conceptual attention, and also distinguishing between stories and poems.
They only test their own models on MRCR for the benchmark graph, but it's still worth reviewing: the accuracy curves are super interesting. https://openai.com/index/gpt-4-1/
kzawpl•7mo ago