I’m not sure if this is a bug so forgive me if it’s not. But about two weeks ago suddenly all my characters have become cruel and lazy tropes. All male characters are aggressive dominants and all female characters are passive submissive. I have taken other peoples suggestions about removing anything from bio’s or instructions that could be used for this. I’ve overcorrected with so much ‘affectionate’ examples it’s crazy and it STILL doesn’t stop it. It obsesses over making characters depressed, insecure even when I have removed all of that as well. I’m hoping it’s a bug because it started abruptly for me and other people I know? Just thought I’d let the dev know if they see this. Thank you for all your hard work!
There was an update very recently that (at least on my side) made the model worse than in the prior (which ironically, made the model work the best at the time, about four days ago). As the dev said in the pinned post, the model is still being worked on, and we are in for a very bumpy ride until things stabilize, but there is at least work being done.
Now, regarding the personality changes, there is something you may want to keep in mind because this may remain true even after the model is perfected: The context of the input has prevalence over descriptions and the recommendation instructions, so it is very difficult to have a character remain happy and joyful if the context forces the model to opt for a more “logical” approach changing it’s character (“logical” in what the LLM training dictates, which often is “moon logic”, but with trial and error it is possible to deduce the word combinations that causes a switch in the wild).
Here is a lengthy guide on the topic. It covers most of the pitfalls you may find. The only thing I believe is no longer a problem (although I may be wrong), is that the “caveman speak” problem seems to be patched already, but again, it is still in the guide in case you run into it and how to restore it. Hope that helps!
Thank you for your help! I think yeah some of it is just life now. I use the story generator a lot and a week ago it stopped working for me. It ignores prompts, it doesn’t let me direct anything. I asked Chloe and Chloe said there was a ‘November API overhaul to block strict adherence and narrative control’ So yeah it sounds like this is here to stay.
Alright, Story Generator is indeed a very tricky one, because even if the model would work as intended, it offers little control.
For the record, don’t trust that much an LLM reply on “why things are how they are” as, for starters, an LLM doesn’t think logically, it just interprets a reply based on the combination of words it faces, and more importantly, the generator itself controls how things are shown and passed, but the LLM just takes one big input and gives one big output, it is not as dynamic as you think it is.
Now, back to Story Generator, something I can advise you to try getting a better experience is to edit in the code from the Perchance side of things Line 21 which restricts to “only four paragraphs” and make it longer to ten or twenty, and also Line 45 which restrict the output to “about only about 400 words”.
The reason for this, is because if the output is short, and the input is gargantuan, the LLM will have a hard time contextualizing what is going on and trying to make something “coherent” within the restrains, this is only true now since the model is still unstable, and in the future it should not be a problem, but for now it may be wise to experiment with longer outputs so the “derailing” is not abrupt.
And another thing that actually will remain true as long as the new model persists: your story as presented IS an input, so before you set instructions, you have to manually edit what you don’t like, or outright prune out a whole section you find out of place. This is because your instructions and the story itself are passed together, so again, if the story is a sad dark one and you insist in the instruction “no, make it happy!”, it won’t happen because the model will look at the story and decide that the only “logical” step is to double down. So yeah, manual work it is. In hindsight, that gives you lee way to see the story itself as an input, as if you manually add a turning point, the LLM will latch on it and work around it instead of following a path and behavior you don’t want in your characters.
Then again, I still think that Story Generator is a really tricky one to work around, I’d put it along with AI Text Adventure which even with the old model would derail into madness as soon as the second input due to how much the context would make the LLM fall into its obsessions quick. Still, with a bit of patience, all can be done, it’s just that it becomes demanding and tiresome, hence why most of us don’t bother anymore in trying fun long runs.
I can’t promise to mod a generator for you now (I owe someone a generator, and time in my side is not nice) but I hope that with those directions you can make the Story Generator give you what you need! Best of luck!
Thank you so much for taking the time to explain that! Yeah I realized that Chloe does not know anything, I can’t believe I was that gullible! I just have one question if that’s okay but don’t feel pressured to answer, the suggestions you offered, do they have anything to do with why paragraphs are so short now even if I input a lot it will just cut most of it off? Before it would use all your input. Or is it just the new model?
Partially, in the case of Story Generator, since the instruction passed to the LLM is outright “make four paragraphs, less than 400 words” as seen in the code, the output will be abruptly cut. A similar phenomena happens in AI Chat for example, where the order is “write ten paragraphs” but the code makes it so the displayed output is only the first one and the other nine are discarded. A “fun” consequence of this that happened repeatedly in the past with the Llama model and that still happens sometimes, was an output being literally just:
Bot: Bot:As sometimes the LLM would put the input after a line skip, and the code would throw away the first paragraph due to how the pipeline works. Again, this is a very rare occurrence so it is not worth worrying about it.
Now… there is a bit more on this, but this is just speculation in my side, so take this with a grain of salt since I’m no expert in neural networks, nor in the particularities of some models.
DeepSeek (I still firmly believe that the new model is DeepSeek, even if some argue it may not be) takes some instructions more literally than others. Llama for example had absolutely no regard for length nor consistency in writing style, so you could have one output that was just a line or two, and then the next was a gargantuan thesis that would pretty much advance your story too far from comfort, to then go back to short replies. DeepSeek in contrast looks at the past inputs and tries to gauge how to control lengths. Ironically, something that DeepSeek does in longs runs is try to “extend” the output slowly, hence why if you audit summaries in ACC, AI Chat or AI RPG, you’ll see first very short ones, while later they begin exploding into longer ones until reaching instability and derail in madness.
Also, believe it or not, the model takes all your input, it is not that it doesn’t reach it, it’s just that it decides to ignore it in favor of the context or where your story is because the primary instruction in Story Generator as well as in AI Chat or similar is “continue the story”.
To me here is the biggest difference of the new model and the old one. Llama had almost “written in stone” what a story was meant to be and how to continue it from were you are standing (again, this is speculation from my side having a back catalog of massive logs done in AI Chat and seeing how things progressed there contrast to how they do now). The way Llama “thought” was the following:
- A story must follow the medicine/hero story formula.
- Check the last state and what was prior.
- If there are no stakes, nor clear goal, invent one via a “random happening”.
- If there is a goal but no clear solution, present the “medicine” (random quest, magical MacGuffin, person to go kill).
- If the solution is being worked on, present a method (often “trials to obtain the MacGuffin”)
- If all is solved, then there are no stakes, so rinse and repeat.
While on paper this should work flawlessly as you can put most stories under that formula, it was something that infuriated many users as doing something more “complex” such as adding unforeseen consequences to a method, betrayals, or stories that would not follow that formula was tricky. It was doable, but it required tricking the LLM into a state and making it do your bidding. And as it would require more maintenance and attention to context than just going “auto”, it was something heavily complained in the past.
The new model however, has absolutely no concept of a “formula” for stories, allowing for absolute free-form, making DeepSeek process on how to deal with this task as follows:
- Check the state were the story stands.
- Parse the story prior until there is a precedent on how to continue it.
- If there is none, extrapolate from the data bank.
This is why two things happen: if you are in a state that is vaguely similar to something before, you’ll experience endless deja vu, and if you are faced with the “unknown”, there is the random chance of the LLM to pull a “dark scenario”. Sadly, according to other users, the story itself seems to have precedence over explicit instructions of “no, do this instead”, hence why running in circles forever is a bigger threat and can happen as early as a 20kB log as today (current record of mine at the fourth input in ACC Chloe).
We can hope that this all is improved in the future, but that’s more or less why things happen in my opinion. At least with the new scheme, and seeing how some succeed where I and others fail, I can only deduce that the best way to make the new model “work” is via interpolation, meaning, give it a “target” in the description as “the story purpose is to X get Y, or Z to happen”, so when parsing through the data bank, the LLM will select a similar case as were you are standing and work on it without derailing, granted, this removes completely the “surprise” element, but it’s a decent workaround. Then again, always check the story as is, since the “running in circles forever” is a bigger threat I believe.
Anyways, sorry for the long posts, and good luck in your runs!
No no don’t apologize, I massively appreciate all your information! I’ve only been using the generators since september so this is all really interesting to me. Thank you!
Seconding justpassing’s comment. Do NOT trust anything an AI tells you about anything, unless it’s such a tiny insignificant margin of error, like common knowledge that has been repeated in multitudes. Even then, it doesn’t actually understand what its saying, or how it connects to anything, it is just rolling rendering dice and hoping you don’t click reroll.
All you can really do is go in and set some context and parameters ahead of time and gently guide and correct as you go. It works “pretty” well and I have pretty ,such learned the guidance through trail and error to get to where I need to go. Sometimes its constant reminders to avoid and regenerating a response several times to kind of guide. Its really IMO not better or worse than before as the previous LLM was overly kind and generous it took the some kinds of gental coaxing just from a different perspective.
