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That definitely makes it seem like it's noticing a great deal of its context window. impressive.

Wow. Cool. I have access to that model and have also seen some impressive context extraction. It also gave a really good summary of a large code base that I dumped in. I saw somebody analyze a huge log file, but we really need something like this needle in a needlestack to help identify when models might be missing something. At the very least, this could give model developers something to analyze their proposed models.

The article shows how much better GPT-4o is at paying attention across its input window compared to GPT-4 Turbo and Claude-3 Sonnet.

We've needed an upgrade to needle in a haystack for a while and this "Needle In A Needlestack" is a good next step! NIAN creates a prompt that includes thousands of limericks and the prompt asks a question about one limerick at a specific location.

BTW, how did you manage all of the throughput to the models and navigate the various throttling strategies for all the models you mentioned?

Looks really cool and useful. Seems like GPT-4o it's a lot better than 4.

Sure thing! Very enjoyable to have people use our work.

Glad to be of assistance! Yeah, It really annoyed me that this critical information was not listed in any one particular spot.

Thanks! Took me a year to discover the key nut there. L1 vs L2 regularization is not well described I found so I went nuts trying to nail it down.

Thanks. It's morphed over time as we add functionality so it's less clean than before.

That's a good idea. thanks!

Thanks! It took forever to bash my way to victory on that trees. The lib also supports the shallow trees in boosting machines.

Looks like my ANTLR 4 reference book is available, if anybody is interested.