Long Context is great and all, but it sucks that all of these LLM's have really poor output length. If I feed something an entire book and ask for a comprehensive summary then I'm expecting at least a full 3-page summary. I get that they try to force these things to be "concise" to save on compute, but good lord it's so annoying.
Have you tried asking it for a specific concrete length, like a number of words? I was also frustrated with concise answers when asking for long ones, but I found that the outputs improved significantly if I asked for e.g. 4000 words specifically. Further than that, have it break it down into sections and write X words per section.
Yes, all the possible length extending custom instructions you can think of. I can get some reasonable length responses out of it, but I've never seen them go over 1 page worth, and multi-shot example prompts using multiple USER and GPT exchanges to define the format. Seems like GPT4 has a hard limit as to how much it will output when you click "continue", and Claude Opus never goes over a page either. Another user pointed out using the API, which I have done in the past, but it's been a long while, and I can't really justify the cost of using the advanced models via API for my general use.
Everyone's coalescing at a max of 4096 tokens/12 "pages" via API (page is 250 words, which is 1 8.5"x11" double spaced)
To your point, doesn't matter anyway, it's nigh impossible to get over 2K of output with every trick and bit of guidance you can think of (I got desperate when 16K/48 pages came out to "make it work", even completely deforming tricks like making it number each line and write a reminder on each line that it should write 1000 lines don't work)
I wouldn't say that, my latest big user story for making sure I'm handling huge inputs was "translate Moby dick to zoomer". Cant give any service chunks larger than ~5K tokens, over API, without it failing.
(Miserably, like, I'd be fine if it gave a paragraph back. But at least on this "map" task, there's a critical point where there's so much input that the reward function ends up imitating the input more instead of chatting)
