Now I'm just imagining a video game with characters each having their own fine tune applied on top for their dialog. I'm guessing you could use some relatively small models. In each case you would be feeding all the context to the model (player name, current relevant quests, summary of previous interactions, etc). Though maybe fine tuning/training isn't even needed and a good enough prompt will work (Not sure what all they used for this [0]). I'm excited for the first AAA game that tries this. Anyone that has played a RPG-style game knows that after a few times going into a city (or a couple play-throughs) the dialog feels repetitive. I love the idea of Skyrim but with better dialog. You could either run the models on the user's computer or maybe just run it on the backend so you can block certain generations (wrong/misleading/"unsafe") and just ship updated dialog lists to the client occasionally.
Counterpoint: NPCs repeating their dialogue serves as an implicit indicator that you've exhausted their content and it's time to move on. If they gain the ability to make vapid smalltalk forever then you'll forever be second guessing whether you're wasting your time on them.
(also spare a thought for the poor QA testers who would be given the Sisyphean task of making sure an LLM dialogue system always stays in character and doesn't hallucinate non-existent or outdated content/lore/mechanics)
That is an issue for the mostly transactional NPCs that make up the majority of NPCs in RPGs. But consider the case of the companion NPC.
If I travel with Batu the Hun and meet Zaya the Hun Slayer I want to be able to ask Batu if I should kill Zaya on the spot or should entertain his offer. That kind of stuff is extremely valuable both for the connection between player and companion and to provide an in-world perspective on the events you witness and the actions you take. But it's also extremely time-intensive to script. It's also very low stakes, it is essentially small-talk. And with some careful design you can limit it to short exchanges with AI-provided dialogue choices and have it distinguishable from scripted dialogue that advances the story
I think there certainly are other, better, more natural ways this could be achieved.
For example, if you're instructing an LLM to portray a character, instead of repeating dialogue like a broken record when they run out of relevant things to say... instruct them to act like their character would.
They might wonder out loud if there's anything else you want to know, or literally let you know that you're acting weird and awkward, etc.
Pair w/ a journaling system so that you can review their dialogue without talking to them and asking the same thing 50 times. Etc.
also spare a thought for the poor QA testers
This doesn't seem entirely unsolveable given strict enough system prompts.
re: QA, besides a strict prompt, I'd imagine it would be hard for AI responses to go truly off the rails if the player's input is limited to "press A to talk" or pick one of 3 dialog options.
Great point, although fixed player input options might sort of defeat the benefit of using LLM to achieve a more organic dialogue flow?
Maybe there could be a hybrid system - choose from suggested responses, or type your own.
I also have vague thoughts of a dialogue system that rewards true role-playing, as in rewarding the saying things aligned with what your character might feasibly say. (Like a more freeform version of the dialogue/RP/reward system in Disco Elysium)
You'll also ask yourself whether any NPC tells you anything of relevance. If there is no intention behind the words why would it be interesting to talk to them in the first place.
As I'm imagining it the NPC LLMs would be trained exclusively on the in-game lore as well as given system prompts to shape what they can and cannot say at any given moment.
something like
---
"You are Bob the Farmer. You grow rutabegas in the Kingdom of Foo. You are a cautious, fearful man. Several years ago your home was pillaged by King Foo and your family was taken. [blah blah blah several more paragraphs of biographical information]
Your primary motivation is to get your family back and keep the farm going so that you don't starve.
Last week you saw a mysterious group of figures in the woods who appeared to be headless. This is bothering you, along with the stress of your missing family. You wish a band of strong warriors could investigate, especially if they have a necromancer among them.
You may discuss any of the general world knowledge in background_lore.txt
You know nothing about the following topics: [[insert list of plot points that haven't happened yet or are unknown to him]] and will become confused, fearful, and slightly belligerent when asked about them."
---
You could of course update the system prompts for each character as the events of the game progress.
It would be a lot of work to keep these system prompts updated and relevant, for every character, as game events progress, but I think some of this could be handled by some kind of inheritance system.
Suppose "Bob" lives in "Town A", which is in "Kingdom B." You could probably define a lot of things at the Town/Kingdom level. Like suppose "Kingdom B" is plagued by orcs, but "Town A" is kind of a citadel that is safe against orcs. "Bob"'s system prompt could inherit a lot of concerns and knowledge from "Town A" and "Kingdom B"... the system would not have to be strictly hierarchical either.
This is where emergent behaviors within a game's world building becomes very interesting. Perhaps asking the right questions leads to a quest line not previously discovered or triggers social actions in support of/against the player.
Not every NPC would have something deeper to offer, much like not everyone in our world would have something that would pique my interest (in a general sense -- I'm sure I could learn something from anyone I spoke with), but it would also make me interested in conversations with NPCs at a deeper level than I currently engage with.
Most times I just talk to obviously unimportant NPCs so that I can read about the setting and feel more immersed in the fiction. It also stems from old RPGs like the original Pokemon where sometimes you had to talk to a random NPC in town to learn how to progress past an obstacle.
I think there's a really interesting opportunity for a synthesis of the classic NPC dialog menu and a fully freeform LLM character.
Namely, the dialog would still be fixed, where there's a pre-defined flow of the conversation and fixed set of facts to deliver to the player. But the LLM could generate variations on it each time so it's never exactly the same twice. And it could add more character so the NPC gets frustrated if you keep asking it over and over. Or, it tries to dumb it down for you. Or, it gets sick of you and just tells you point blank: Look, you need to know XYZ. That's all I have for you.
Namely, the dialog would still be fixed, where there's a pre-defined flow of the conversation and fixed set of facts to deliver to the player. But the LLM could generate variations on it each time so it's never exactly the same twice. And it could add more character so the NPC gets frustrated if you keep asking it over and over. Or, it tries to dumb it down for you. Or, it gets sick of you and just tells you point blank: Look, you need to know XYZ. That's all I have for you.
Or if it's important pre-scripted text you could put a different colored border around it or include an option like, "What was that thing about the thing that you said" as a permanent option to allow the player to re-trigger the script if needed.
I like this idea a lot. Alternatively, perhaps a dialogue journaling system that records the important bits for you and can be reviewed at any time, instead of badgering the character in-game to repeat things.
No, this is smart, because then the journal could also be the thing that records all of the AI's made up on the spot sayings.
If you indexed that with the unique character descriptor then the LLM can be programed to re-read the relevant data other iterations of itself made and keep the characters somewhat consistent.
Maybe allow it other information like:
"How many in game days since last conversation" after a random number would cause the npc to forget the character
"What major events have happened in the world, if any"
"What events have happened in this area"
"Generic gossip related to this character"
"What other NPCs are doing at this time"
All of these combined into summary paragraphs would go a long way to influence what topics would be on the characters mind while also not requiring that every NPC has a perfect knowledge of all events or that the game designers have the script everything out.
Could also cause some interesting emergent gameplay, both naturally by the game world evolving over time as the characters play in it, moreso if it's a MMO.
Could also be fun to do things like advance the game clock 1,000 years and see what happens to the npcs.
What if the world is fully destructible? What if the NPCs can build new houses, expand cities, engage in trade? What if NPCs can die of natural causes, get married, have children that are genetic descendants of their parents? What if NPCs undertook quests and occasionally advanced the plot all on their own?
What if you were just one person among hundreds of thousands in the game world?
It’s a really good point. One thing which comes to mind is the way some games distinguish between UI blocking dialog and background color, which could be a great place to start: imagine walking through a city like Baldur’s Gate only it has actual thousands of people who are saying different things when you walk by, and some of those are based on things your party has done recently with specific details about appearance, gear, and actions which would be too hard to do with traditional dialog approaches (e.g. kids talking about a battle and who they thought was best like real kids talk about sports, a good priest wondering what a paladin was doing spotted talking to a notorious thief, etc.). Something like that could add color and immersion without affecting gameplay or wasting anyone’s time, and you could extend it to things like vendors (“saw you put that axe to good use…” or “were you wearing these boots when you freed those slaves? I bet my brother will want buy them!”) to flesh out the approach before using it for load-bearing purposes.
I really like that idea, the "passive" dialog can use the LLM but main dialog is a little more "on the rails". IIRC Skyrim has "background" dialog change subtly throughout the game as you progress but after 1-2 times hearing it, it feels repetitive. Using LLMs to keep that "fresh" would be interesting.
> a good priest wondering what a paladin was doing spotted talking to a notorious thief, etc.)
There's a thing called "prefix tuning" which is basically like a prompt but in a latent space: i.e. prompt which consists of vectors (either key and value vectors, or just input embedding vectors - like custom tokens).
Unlike regular prompts you can optimize them exactly the way you'd do fine-tuning, i.e. if you have examples you can tune your latent prompt to match them as close as possible. I guess the benefit is that you can match style more closely and it would be less artificial. Also they can be rather compact.
Another option is to keep a bunch of LoRA adapters which can be dynamically selected. They can also be very compact.
If you play Fortnite right now (until Friday June 7th). You can speak in realtime to Darth Vader, he replies in his voice and in character, he knows who’s playing (the name of the character skin). The technology is here, and used in production of major games. It’ll be a big tide sooner than what people expect.
The idea of using "smaller LLMs" to control the agency of RPG characters has been a pretty common one ever since AI Dungeon back in 2019. The hardest aspect of it would be locking the AI down to a well-defined character dossier so that it is hard to jailbreak them, and also to limit knowledge leakage of things they don't know, etc.
It would also lend itself very well towards interactive fiction, point-and-click adventure games, etc.
I've been thinking really hard about this for a while, though I don't have any game development experience.
Especially if you pair it with a capability like the voice interface of ChatGPT which I find very impressive in terms of intonation and realism.
It would not need to cut humans out of the loop. You would have humans writing the prompts, recording voices, etc. (I assume the synthetic voices used by ChatGPT are based at some level on recordings of humans. Correct me if I'm wrong.)
Good question - my best assessment is just the text classifier. IE was the LLM able to “trick” the classifier into believing the text came from the IPJ?
And it came quite a long way in training. Initially the classifier scores were very low (mean around 0.05, meaning modern). Over training, the scores came up and ended close to 0.95 (IPJ). The standard deviation of the group also declined, so the consistency of responses improved as well.
My thought on the application of this is that you could use it to create different voices to your responses and probably even add multiple at a time to a single model. I chose this one to experiment, because it is easy to classify and the data was available in the public domain.
GRPO kind of opens up RL to lower tiers of hardware and I’ve been able to experiment with it at home. I think this is something people can do themselves and it’s fun and potentially useful in games or possibly in relation to applications interfacing kids with lower reading levels (eg using a reading level classifier instead).
Yet, one might justly question the imperative of cultivating a distinct model for such an endeavour, when a judiciously framed prompt, enriched by apposite examples, might suffice to imbue a sophisticated engine with the desired stylistic graces. Though it is undeniable these modern engines shall wax greatly in their proportions, and the art of discovering the exact prompt to elicit their most felicitous expressions is a task far from trivial, yet, it must be admitted, the pursuit holds a certain diversion for the inquisitive mind! It is, perchance, not the creation of manifold engines, but rather the artful disposition of singular contexts, that shall bestow upon diverse interlocutors their proper and unique voices.
Love it. Immediately reminded of the text filters back in the day like the pirate one that would drop letters and replace with apostrophes and change certain passages into "arr" or "yarr matey"
This is really cool! Do you have any of the pipeline code available that you used for training? I am curious about how you created the reward model. I love little projects like this, thanks for sharing. I've been fine-tuning on my mac and an interested in getting into GRPO, which I haven't tried yet.
You mention no supervised finetuning. May I ask why? I'm curious if you could get similar/better/worse results by just finetuning the LLM on your dataset rather than generating synthetic data, training a classifier and using GRPO?
In some other experiments, I’ve noticed that SFT can be very rigid and hard to generalize from, and later found this paper: https://arxiv.org/abs/2501.17161
Also I wanted to start from the instruct model, and wasn’t certain if it would be a good idea to do continued pretraining over top of it. Otherwise I’d need to create an instruct dataset for which the passages from the IPJ would be the answer… that also seemed a bit unnatural.
Perhaps there’s still some viable pathways in there, but I decided to see how the stupid simple thing worked out first and just go directly to RL. I think supervised fine tuning is feasible, but it’s not entirely straightforward what to train on and how much data to use if you want to follow up with RL.
I'm not sure if you've tried this already, but removing the translate step might give you a more authentic output. In the journals that I saw, the language was much more simple than the output.
Have you written anywhere in detail on how you gathered your dataset and trained the finetune? I have a few use cases that are like this, but I'm not sure where to start.
It’s fairly simple — I essentially just split the original text into chunks and then used some bigger models on openrouter to clean it up and provide translations to modern English (seemed to be pretty easy for an LLM).
After that, I just trained a MiniLM2 model to classify the texts. I used this in a reward function for reinforcement learning and changed the system message as a simple instruction to write in the prose of the IPJ.
I debated whether or not to use any SFT, and decided not to. I think if the style would be too hard to learn you might need some seed/cold start SFT data.
I’ll try to get my scripts up in github for you to look at. It’s just a few short training scripts.
Thanks for the explanation! I'm learning and and think this would be a good next project for me to try, especially since I have a real world use case in mind with a similar amount of data available.
In particular, I'm not very familiar with reinforcement learning and am not sure how you use the embeddings from MiniLM2 as a reward function. (Edit: maybe this is the jaccard similarity?)
I'd really appreciate it if you were open to posting scripts! I see a few snippets around and could probably cobble something together after a while. But, it's cool to see something already working to make sure I'm not getting too far off into left field.
You can ignore the jaccard similarity field. That was just to monitor the text->cleaned text conversion to make sure it didn’t stray too far from the original while it was fixing whitespace OCR issues.
You can then just load that and train it on your data using a standard transformers classification pipeline. ChatGPT can zero shot that part reasonably well if you gave it this description.
From there you should check out the GRPO trainer in TRL. It has taken me a bit of time to learn how to use it effectively. There’s a TON of parameters in the configuration, and occasionally I have to hunt down arxiv papers to understand them.
I added my scripts to a github repo in case you were interested in seeing how I did it. It’s a bit messy — but fine for a reference. I might try training it again soon with some new ideas, and I’ll polish it up more then.
Not sure this is true. Most of the famous Irish writers were Anglo-Irish Protestants (Yeats, Wilde, Swift, Beckett). Joyce is the notable exception here. The Irish certainly produce great cultural works of the English language (well beyond their size). But also the penal laws greatly depressed the cultural output of the Irish people for 250 years.
I feel that the "best practitioners" is not limited to the most famous writers. A great thing about Ireland is the conversations to be had there, and how quick-witted Irish people often are, with clever use of the language. This can be true elsewhere in the English-speaking world, but Ireland has some renown for it.
Kinda of strange to pick an example that is just wrong. It's supposed to be written from 1840 and says Paris is the seat of Napoleon almost 20 years after he died.
Its considered an LLM tell because its a term that is rarely used by the median modern casual writer; its not at all uncommon even in current literature [0], and its even more common in older literature, so complaining about it in a model designed to reproduce a particular style of 19th century print literature is silly in the extreme.
[0] many “LLM tells” fit this pattern of just being common features of professionally-published works that are less often seen in casual writing.
FWIW, the 19th century style dataset this is trained on doesn't seem to have any examples of delve [1], with the exception of:
> It was into that bank that the creature had
worked its way, and on listening I could hear it delving and scraping at
a great rate, about a yard from the back of the wall.
I bring that up to point out that this isn't necessarily (more) common in the 19th century style print literature, so the observation might not be silly. The model creating the modern synthetic version injected 'delve' 9 times, which implies that it is more frequently used in modern literature or just something that models tend to inject. Though, I could be missing something (either in searching the data set, or how this works).
It's very common in fantasy novels - dwarves and wizards do a lot of delving into caves, dungeons, and towers. It's also a solid academic term, so scientists delve into a lot of subjects, and brain people do a lot of delving into psyches.
LLMs are raising the bar by expanding the vocabulary people are exposed to, so words like delve will stick out. I think it's preferred by writers because it articulates a nice sounding alternative to words like explore, venture, analyze, think about, confront, etc. It's a useful, versatile word, and one of the metrics by which writers measure quality is the minimization of syllables.
LLMs are mostly indistinguishable from humans at this point; a one-shot output from any of the major models can be recognized in the same way you might recognize a writer. With multiple style passes, you're not going to be able to tell the difference between ChatGPT, Ronald Reagan, Bill Clinton, Hunter S. Thompson, Einstein, or any other sufficiently modeled figure. Throw in a few tens of thousands of words written by yourself and most of the models will do a nearly flawless job of copying your stylometric profile.
Delving also has the implication of going deep, while exploring has the implication of going wide. I wonder if human authors really do a good job of picking the right word there. Either way I wonder if “misused delve” could be an interesting signal.
It's being looked at as an AI signal, which is causing labs to artificially suppress it, so it may end up being a "human-authored" signal in the end. Give it a year or two, though, and we're looking at AI with superhuman word choice. There will be dozens of layers of introspection underlying the selection of each word, in a broad context, articulating exactly whatever the user wants. The philosophical and psychological implications of superhuman text generation, beyond the p(doom) discussions, gets crazy. Superhuman persuasion is one facet, but unintended manipulation through reinforcement of secondary and peripheral notions in the context create all sorts of weirdness.
Language communicates ideas, and we've made machines that produce intricate, sophisticated ideas that land in our brains. The consequences are going to be fascinating.
Indeed. It's because of this that I have serious concerns about what "AI" is doing to our writing. My son has been flagged for using "AI" in writing his papers because it sounded "too good." But, I know he wrote it. I've run into the same issue where people have suggested something I wrote was AI because of certain vocabulary words that I've used. The only "defense" against this is to write a little bit shitty, because then there is no suspicion. If everybody starts doing that, especially in academic settings, that's a road to serious sadness IMHO.
We’re in a transitional stage at the moment, so I’m not too worried about maladaptive shitty writing becoming the norm (although, it sucks that your kid is being punished for our failure to adapt).
It would be totally nuts to take points off for using spell check. An LLM should be able to provide style check without causing any concerns; it will become the norm, and then too good prose won’t cause any flags to be thrown.
I seriously hate the supposed "tells" for LLM writing. Can't use delve, em dash is suspect, never get a fact wrong, and God forbid your sentence structure is more complex than what a 3rd grader is capable of.
I think we're well beyond the point where the majority of people cannot tell what is actually produced by an LLM and we're convincing ourselves we still have a handle on the situation. A lot of these rules are completely arbitrary and vary from person to person.
>What's the issue with 'delve'? It's hardly Old English
It's used a ton by LLMs for some reason despite being rarely used by real people. I think it's mostly a byproduct of LLMs having their training data being over represented by certain published works instead of casual communications.
There does seem to be something else going on with delve specifically though, one of the other comments mentions that delve isn't used in the specific training data for this, so it's odd to see it being used in the output. I wonder if it's because delve has secondary definitions of "to make a careful or detailed search for information" and "to examine a subject in detail" which is causing the LLM to use it to seem like it's answers are more thorough.
I keep seeing that proposed as a theory, but are companies actually outsourcing this work to Nigeria or is that just an assumption someone made because they know delve is more commonly used there? A lot of these posts can't even decide which African country to blame it on. I've heard Kenya and Nigeria and just 'Africa' in general.
The standard narrative is that chatbot training efforts were all outsourced to poorest regions of Africa with best English fluency, and it turned out they(both their child LLM and the poor guys who trained them) use the word "delve" a lot.
I don't know anything about Africa, but I assumed it's because dictionaries define it to mean "to make a careful or detailed search for information" and "to examine a subject in detail" and LLMs seem to latch on to using it for that for some reason.
If anything, I'd assume the chatbots would use Indian English phrases like "do the needful" and those weird phrases that only make sense in Hindi but are translated to English.
I recall in 1980's, style-analyzing software reporting word counts for words with high frequency in the text but low frequency in general usage - jargon. The suggestion was to re-word, and it was often a relevant clue to re-think by examining why one word was doing so much work. Detecting that sounds like more than repetition, but still feasible probabilistically and relevant at both usage and conceptual levels.
Now I'm just imagining a video game with characters each having their own fine tune applied on top for their dialog. I'm guessing you could use some relatively small models. In each case you would be feeding all the context to the model (player name, current relevant quests, summary of previous interactions, etc). Though maybe fine tuning/training isn't even needed and a good enough prompt will work (Not sure what all they used for this [0]). I'm excited for the first AAA game that tries this. Anyone that has played a RPG-style game knows that after a few times going into a city (or a couple play-throughs) the dialog feels repetitive. I love the idea of Skyrim but with better dialog. You could either run the models on the user's computer or maybe just run it on the backend so you can block certain generations (wrong/misleading/"unsafe") and just ship updated dialog lists to the client occasionally.
[0] https://www.youtube.com/watch?v=d6sVWEu9HWU
Counterpoint: NPCs repeating their dialogue serves as an implicit indicator that you've exhausted their content and it's time to move on. If they gain the ability to make vapid smalltalk forever then you'll forever be second guessing whether you're wasting your time on them.
(also spare a thought for the poor QA testers who would be given the Sisyphean task of making sure an LLM dialogue system always stays in character and doesn't hallucinate non-existent or outdated content/lore/mechanics)
That is an issue for the mostly transactional NPCs that make up the majority of NPCs in RPGs. But consider the case of the companion NPC.
If I travel with Batu the Hun and meet Zaya the Hun Slayer I want to be able to ask Batu if I should kill Zaya on the spot or should entertain his offer. That kind of stuff is extremely valuable both for the connection between player and companion and to provide an in-world perspective on the events you witness and the actions you take. But it's also extremely time-intensive to script. It's also very low stakes, it is essentially small-talk. And with some careful design you can limit it to short exchanges with AI-provided dialogue choices and have it distinguishable from scripted dialogue that advances the story
I think there certainly are other, better, more natural ways this could be achieved.
For example, if you're instructing an LLM to portray a character, instead of repeating dialogue like a broken record when they run out of relevant things to say... instruct them to act like their character would.
They might wonder out loud if there's anything else you want to know, or literally let you know that you're acting weird and awkward, etc.
Pair w/ a journaling system so that you can review their dialogue without talking to them and asking the same thing 50 times. Etc.
This doesn't seem entirely unsolveable given strict enough system prompts.re: QA, besides a strict prompt, I'd imagine it would be hard for AI responses to go truly off the rails if the player's input is limited to "press A to talk" or pick one of 3 dialog options.
Great point, although fixed player input options might sort of defeat the benefit of using LLM to achieve a more organic dialogue flow?
Maybe there could be a hybrid system - choose from suggested responses, or type your own.
I also have vague thoughts of a dialogue system that rewards true role-playing, as in rewarding the saying things aligned with what your character might feasibly say. (Like a more freeform version of the dialogue/RP/reward system in Disco Elysium)
At that point you're back to a pre-baked script.
The implicit indicator is sort of bad, though. I mean, it is a very gamey, immersion breaking thing. We’re just used to it.
Realistically NPCs should probably respond with increasing urgency if you forget their quest, and eventually give up on you.
You'll also ask yourself whether any NPC tells you anything of relevance. If there is no intention behind the words why would it be interesting to talk to them in the first place.
As I'm imagining it the NPC LLMs would be trained exclusively on the in-game lore as well as given system prompts to shape what they can and cannot say at any given moment.
something like
---
"You are Bob the Farmer. You grow rutabegas in the Kingdom of Foo. You are a cautious, fearful man. Several years ago your home was pillaged by King Foo and your family was taken. [blah blah blah several more paragraphs of biographical information]
Your primary motivation is to get your family back and keep the farm going so that you don't starve.
Last week you saw a mysterious group of figures in the woods who appeared to be headless. This is bothering you, along with the stress of your missing family. You wish a band of strong warriors could investigate, especially if they have a necromancer among them.
You may discuss any of the general world knowledge in background_lore.txt
You know nothing about the following topics: [[insert list of plot points that haven't happened yet or are unknown to him]] and will become confused, fearful, and slightly belligerent when asked about them."
---
You could of course update the system prompts for each character as the events of the game progress.
It would be a lot of work to keep these system prompts updated and relevant, for every character, as game events progress, but I think some of this could be handled by some kind of inheritance system.
Suppose "Bob" lives in "Town A", which is in "Kingdom B." You could probably define a lot of things at the Town/Kingdom level. Like suppose "Kingdom B" is plagued by orcs, but "Town A" is kind of a citadel that is safe against orcs. "Bob"'s system prompt could inherit a lot of concerns and knowledge from "Town A" and "Kingdom B"... the system would not have to be strictly hierarchical either.
This is where emergent behaviors within a game's world building becomes very interesting. Perhaps asking the right questions leads to a quest line not previously discovered or triggers social actions in support of/against the player.
Not every NPC would have something deeper to offer, much like not everyone in our world would have something that would pique my interest (in a general sense -- I'm sure I could learn something from anyone I spoke with), but it would also make me interested in conversations with NPCs at a deeper level than I currently engage with.
Most times I just talk to obviously unimportant NPCs so that I can read about the setting and feel more immersed in the fiction. It also stems from old RPGs like the original Pokemon where sometimes you had to talk to a random NPC in town to learn how to progress past an obstacle.
> If there is no intention behind the words why would it be interesting to talk to them in the first place.
But of course there is a story behind them.
I think there's a really interesting opportunity for a synthesis of the classic NPC dialog menu and a fully freeform LLM character.
Namely, the dialog would still be fixed, where there's a pre-defined flow of the conversation and fixed set of facts to deliver to the player. But the LLM could generate variations on it each time so it's never exactly the same twice. And it could add more character so the NPC gets frustrated if you keep asking it over and over. Or, it tries to dumb it down for you. Or, it gets sick of you and just tells you point blank: Look, you need to know XYZ. That's all I have for you.
Namely, the dialog would still be fixed, where there's a pre-defined flow of the conversation and fixed set of facts to deliver to the player. But the LLM could generate variations on it each time so it's never exactly the same twice. And it could add more character so the NPC gets frustrated if you keep asking it over and over. Or, it tries to dumb it down for you. Or, it gets sick of you and just tells you point blank: Look, you need to know XYZ. That's all I have for you.
Or if it's important pre-scripted text you could put a different colored border around it or include an option like, "What was that thing about the thing that you said" as a permanent option to allow the player to re-trigger the script if needed.
I like this idea a lot. Alternatively, perhaps a dialogue journaling system that records the important bits for you and can be reviewed at any time, instead of badgering the character in-game to repeat things.
No, this is smart, because then the journal could also be the thing that records all of the AI's made up on the spot sayings.
If you indexed that with the unique character descriptor then the LLM can be programed to re-read the relevant data other iterations of itself made and keep the characters somewhat consistent.
Maybe allow it other information like:
"How many in game days since last conversation" after a random number would cause the npc to forget the character "What major events have happened in the world, if any" "What events have happened in this area" "Generic gossip related to this character" "What other NPCs are doing at this time"
All of these combined into summary paragraphs would go a long way to influence what topics would be on the characters mind while also not requiring that every NPC has a perfect knowledge of all events or that the game designers have the script everything out.
Could also cause some interesting emergent gameplay, both naturally by the game world evolving over time as the characters play in it, moreso if it's a MMO.
Could also be fun to do things like advance the game clock 1,000 years and see what happens to the npcs.
What if the world is fully destructible? What if the NPCs can build new houses, expand cities, engage in trade? What if NPCs can die of natural causes, get married, have children that are genetic descendants of their parents? What if NPCs undertook quests and occasionally advanced the plot all on their own?
What if you were just one person among hundreds of thousands in the game world?
I'd love to play something like that.
_Very_ good point. I had not fully considered that, same deal with conversation trees vs free-form entry/response.
It’s a really good point. One thing which comes to mind is the way some games distinguish between UI blocking dialog and background color, which could be a great place to start: imagine walking through a city like Baldur’s Gate only it has actual thousands of people who are saying different things when you walk by, and some of those are based on things your party has done recently with specific details about appearance, gear, and actions which would be too hard to do with traditional dialog approaches (e.g. kids talking about a battle and who they thought was best like real kids talk about sports, a good priest wondering what a paladin was doing spotted talking to a notorious thief, etc.). Something like that could add color and immersion without affecting gameplay or wasting anyone’s time, and you could extend it to things like vendors (“saw you put that axe to good use…” or “were you wearing these boots when you freed those slaves? I bet my brother will want buy them!”) to flesh out the approach before using it for load-bearing purposes.
I really like that idea, the "passive" dialog can use the LLM but main dialog is a little more "on the rails". IIRC Skyrim has "background" dialog change subtly throughout the game as you progress but after 1-2 times hearing it, it feels repetitive. Using LLMs to keep that "fresh" would be interesting.
> a good priest wondering what a paladin was doing spotted talking to a notorious thief, etc.)
Love this and the other examples you gave.
There's a thing called "prefix tuning" which is basically like a prompt but in a latent space: i.e. prompt which consists of vectors (either key and value vectors, or just input embedding vectors - like custom tokens).
Unlike regular prompts you can optimize them exactly the way you'd do fine-tuning, i.e. if you have examples you can tune your latent prompt to match them as close as possible. I guess the benefit is that you can match style more closely and it would be less artificial. Also they can be rather compact.
Another option is to keep a bunch of LoRA adapters which can be dynamically selected. They can also be very compact.
If you play Fortnite right now (until Friday June 7th). You can speak in realtime to Darth Vader, he replies in his voice and in character, he knows who’s playing (the name of the character skin). The technology is here, and used in production of major games. It’ll be a big tide sooner than what people expect.
The idea of using "smaller LLMs" to control the agency of RPG characters has been a pretty common one ever since AI Dungeon back in 2019. The hardest aspect of it would be locking the AI down to a well-defined character dossier so that it is hard to jailbreak them, and also to limit knowledge leakage of things they don't know, etc.
It would also lend itself very well towards interactive fiction, point-and-click adventure games, etc.
I've been thinking really hard about this for a while, though I don't have any game development experience.
Especially if you pair it with a capability like the voice interface of ChatGPT which I find very impressive in terms of intonation and realism.
It would not need to cut humans out of the loop. You would have humans writing the prompts, recording voices, etc. (I assume the synthetic voices used by ChatGPT are based at some level on recordings of humans. Correct me if I'm wrong.)
Here's a demo of "The Matrix" from 2 years ago:
https://youtu.be/4sCWf2VGdfc?t=356
Marvelous! What gain beyond zero-shot would motivate a humble citizen to implement this instrument? How was the superiority assessed?
Good question - my best assessment is just the text classifier. IE was the LLM able to “trick” the classifier into believing the text came from the IPJ?
And it came quite a long way in training. Initially the classifier scores were very low (mean around 0.05, meaning modern). Over training, the scores came up and ended close to 0.95 (IPJ). The standard deviation of the group also declined, so the consistency of responses improved as well.
My thought on the application of this is that you could use it to create different voices to your responses and probably even add multiple at a time to a single model. I chose this one to experiment, because it is easy to classify and the data was available in the public domain.
GRPO kind of opens up RL to lower tiers of hardware and I’ve been able to experiment with it at home. I think this is something people can do themselves and it’s fun and potentially useful in games or possibly in relation to applications interfacing kids with lower reading levels (eg using a reading level classifier instead).
Yet, one might justly question the imperative of cultivating a distinct model for such an endeavour, when a judiciously framed prompt, enriched by apposite examples, might suffice to imbue a sophisticated engine with the desired stylistic graces. Though it is undeniable these modern engines shall wax greatly in their proportions, and the art of discovering the exact prompt to elicit their most felicitous expressions is a task far from trivial, yet, it must be admitted, the pursuit holds a certain diversion for the inquisitive mind! It is, perchance, not the creation of manifold engines, but rather the artful disposition of singular contexts, that shall bestow upon diverse interlocutors their proper and unique voices.
Love it. Immediately reminded of the text filters back in the day like the pirate one that would drop letters and replace with apostrophes and change certain passages into "arr" or "yarr matey"
Reminds me of this: https://www.unix.com/man_page/debian/6/jive/
This is really cool! Do you have any of the pipeline code available that you used for training? I am curious about how you created the reward model. I love little projects like this, thanks for sharing. I've been fine-tuning on my mac and an interested in getting into GRPO, which I haven't tried yet.
I put my scripts up in github. It’s a bit scrapped together at the moment — but fine as a reference.
https://github.com/dleemiller/PennyLM
what a wonderful work of whimsy! well wrought.
I'd love to have a library of these, so I could pipe text into `penny`, `brainrot`, `pony`, `newspeak`, `corporate`, `scp`, `trek` etc.
have you published the training notebook somewhere?
You mention no supervised finetuning. May I ask why? I'm curious if you could get similar/better/worse results by just finetuning the LLM on your dataset rather than generating synthetic data, training a classifier and using GRPO?
Cool stuff in any case.
In some other experiments, I’ve noticed that SFT can be very rigid and hard to generalize from, and later found this paper: https://arxiv.org/abs/2501.17161
Also I wanted to start from the instruct model, and wasn’t certain if it would be a good idea to do continued pretraining over top of it. Otherwise I’d need to create an instruct dataset for which the passages from the IPJ would be the answer… that also seemed a bit unnatural.
Perhaps there’s still some viable pathways in there, but I decided to see how the stupid simple thing worked out first and just go directly to RL. I think supervised fine tuning is feasible, but it’s not entirely straightforward what to train on and how much data to use if you want to follow up with RL.
I'm not sure if you've tried this already, but removing the translate step might give you a more authentic output. In the journals that I saw, the language was much more simple than the output.
Have you written anywhere in detail on how you gathered your dataset and trained the finetune? I have a few use cases that are like this, but I'm not sure where to start.
My dataset is here: https://huggingface.co/datasets/dleemiller/irish_penny_journ...
It’s fairly simple — I essentially just split the original text into chunks and then used some bigger models on openrouter to clean it up and provide translations to modern English (seemed to be pretty easy for an LLM).
After that, I just trained a MiniLM2 model to classify the texts. I used this in a reward function for reinforcement learning and changed the system message as a simple instruction to write in the prose of the IPJ.
I debated whether or not to use any SFT, and decided not to. I think if the style would be too hard to learn you might need some seed/cold start SFT data.
I’ll try to get my scripts up in github for you to look at. It’s just a few short training scripts.
Thanks for the explanation! I'm learning and and think this would be a good next project for me to try, especially since I have a real world use case in mind with a similar amount of data available.
In particular, I'm not very familiar with reinforcement learning and am not sure how you use the embeddings from MiniLM2 as a reward function. (Edit: maybe this is the jaccard similarity?)
I'd really appreciate it if you were open to posting scripts! I see a few snippets around and could probably cobble something together after a while. But, it's cool to see something already working to make sure I'm not getting too far off into left field.
You can ignore the jaccard similarity field. That was just to monitor the text->cleaned text conversion to make sure it didn’t stray too far from the original while it was fixing whitespace OCR issues.
I didn’t use embeddings. Nreimers account on huggingface has the minilm models which are BERT-like, but trained using distillation. https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-f... Is the one I started from.
You can then just load that and train it on your data using a standard transformers classification pipeline. ChatGPT can zero shot that part reasonably well if you gave it this description.
From there you should check out the GRPO trainer in TRL. It has taken me a bit of time to learn how to use it effectively. There’s a TON of parameters in the configuration, and occasionally I have to hunt down arxiv papers to understand them.
Ah! That makes more sense. Thank you for clarifying. Great to see you got so much activity on your HN thread here.
I added my scripts to a github repo in case you were interested in seeing how I did it. It’s a bit messy — but fine for a reference. I might try training it again soon with some new ideas, and I’ll polish it up more then.
https://github.com/dleemiller/PennyLM
this is awesome
It is sort of funny that the Irish ended up being the best practitioners of the English language, despite the fact that they were forced to use it.
Not sure this is true. Most of the famous Irish writers were Anglo-Irish Protestants (Yeats, Wilde, Swift, Beckett). Joyce is the notable exception here. The Irish certainly produce great cultural works of the English language (well beyond their size). But also the penal laws greatly depressed the cultural output of the Irish people for 250 years.
I feel that the "best practitioners" is not limited to the most famous writers. A great thing about Ireland is the conversations to be had there, and how quick-witted Irish people often are, with clever use of the language. This can be true elsewhere in the English-speaking world, but Ireland has some renown for it.
Joseph Conrad also had English as a second language.
Kinda of strange to pick an example that is just wrong. It's supposed to be written from 1840 and says Paris is the seat of Napoleon almost 20 years after he died.
It's transferring the style, not the knowledge.
Nice work ! It still manage to use the word 'delve' in the first sentence, which is a giveaway that it's written by a LLM.
Its considered an LLM tell because its a term that is rarely used by the median modern casual writer; its not at all uncommon even in current literature [0], and its even more common in older literature, so complaining about it in a model designed to reproduce a particular style of 19th century print literature is silly in the extreme.
[0] many “LLM tells” fit this pattern of just being common features of professionally-published works that are less often seen in casual writing.
FWIW, the 19th century style dataset this is trained on doesn't seem to have any examples of delve [1], with the exception of:
> It was into that bank that the creature had worked its way, and on listening I could hear it delving and scraping at a great rate, about a yard from the back of the wall.
I bring that up to point out that this isn't necessarily (more) common in the 19th century style print literature, so the observation might not be silly. The model creating the modern synthetic version injected 'delve' 9 times, which implies that it is more frequently used in modern literature or just something that models tend to inject. Though, I could be missing something (either in searching the data set, or how this works).
[1] https://huggingface.co/datasets/dleemiller/irish_penny_journ...
It's very common in fantasy novels - dwarves and wizards do a lot of delving into caves, dungeons, and towers. It's also a solid academic term, so scientists delve into a lot of subjects, and brain people do a lot of delving into psyches.
LLMs are raising the bar by expanding the vocabulary people are exposed to, so words like delve will stick out. I think it's preferred by writers because it articulates a nice sounding alternative to words like explore, venture, analyze, think about, confront, etc. It's a useful, versatile word, and one of the metrics by which writers measure quality is the minimization of syllables.
LLMs are mostly indistinguishable from humans at this point; a one-shot output from any of the major models can be recognized in the same way you might recognize a writer. With multiple style passes, you're not going to be able to tell the difference between ChatGPT, Ronald Reagan, Bill Clinton, Hunter S. Thompson, Einstein, or any other sufficiently modeled figure. Throw in a few tens of thousands of words written by yourself and most of the models will do a nearly flawless job of copying your stylometric profile.
Delving also has the implication of going deep, while exploring has the implication of going wide. I wonder if human authors really do a good job of picking the right word there. Either way I wonder if “misused delve” could be an interesting signal.
It's being looked at as an AI signal, which is causing labs to artificially suppress it, so it may end up being a "human-authored" signal in the end. Give it a year or two, though, and we're looking at AI with superhuman word choice. There will be dozens of layers of introspection underlying the selection of each word, in a broad context, articulating exactly whatever the user wants. The philosophical and psychological implications of superhuman text generation, beyond the p(doom) discussions, gets crazy. Superhuman persuasion is one facet, but unintended manipulation through reinforcement of secondary and peripheral notions in the context create all sorts of weirdness.
Language communicates ideas, and we've made machines that produce intricate, sophisticated ideas that land in our brains. The consequences are going to be fascinating.
Indeed. It's because of this that I have serious concerns about what "AI" is doing to our writing. My son has been flagged for using "AI" in writing his papers because it sounded "too good." But, I know he wrote it. I've run into the same issue where people have suggested something I wrote was AI because of certain vocabulary words that I've used. The only "defense" against this is to write a little bit shitty, because then there is no suspicion. If everybody starts doing that, especially in academic settings, that's a road to serious sadness IMHO.
We’re in a transitional stage at the moment, so I’m not too worried about maladaptive shitty writing becoming the norm (although, it sucks that your kid is being punished for our failure to adapt).
It would be totally nuts to take points off for using spell check. An LLM should be able to provide style check without causing any concerns; it will become the norm, and then too good prose won’t cause any flags to be thrown.
I delve into facts and case law frequently.
I seriously hate the supposed "tells" for LLM writing. Can't use delve, em dash is suspect, never get a fact wrong, and God forbid your sentence structure is more complex than what a 3rd grader is capable of.
I think we're well beyond the point where the majority of people cannot tell what is actually produced by an LLM and we're convincing ourselves we still have a handle on the situation. A lot of these rules are completely arbitrary and vary from person to person.
Skullface sends his regards:
https://arxiv.org/abs/2409.01754
https://youtu.be/sy4SwW0QkoA
It's a tell because it's a pattern. The word "delve" has seen its used skyrocket after ChatGPT came out. It's not some kind of strange coincidence.
Bullet points are taboo now.
It's becoming fairly upsetting to see words I like using end up on lists of AI identifiers.
RIP kids who grew up digesting hundreds of fantasy novels and playing D&D.
What's the issue with 'delve'? It's hardly Old English
>What's the issue with 'delve'? It's hardly Old English
It's used a ton by LLMs for some reason despite being rarely used by real people. I think it's mostly a byproduct of LLMs having their training data being over represented by certain published works instead of casual communications.
There does seem to be something else going on with delve specifically though, one of the other comments mentions that delve isn't used in the specific training data for this, so it's odd to see it being used in the output. I wonder if it's because delve has secondary definitions of "to make a careful or detailed search for information" and "to examine a subject in detail" which is causing the LLM to use it to seem like it's answers are more thorough.
> It's used a ton by LLMs for some reason despite being rarely used by real people.
The popular theory is that it's due to English-language RLHF tasks being outsourced to Nigeria, where "delve" is used relatively often.
https://simonwillison.net/2024/Apr/18/delve/
I keep seeing that proposed as a theory, but are companies actually outsourcing this work to Nigeria or is that just an assumption someone made because they know delve is more commonly used there? A lot of these posts can't even decide which African country to blame it on. I've heard Kenya and Nigeria and just 'Africa' in general.
It's "lexically overrepresented". https://arxiv.org/abs/2412.11385
The standard narrative is that chatbot training efforts were all outsourced to poorest regions of Africa with best English fluency, and it turned out they(both their child LLM and the poor guys who trained them) use the word "delve" a lot.
I don't know anything about Africa, but I assumed it's because dictionaries define it to mean "to make a careful or detailed search for information" and "to examine a subject in detail" and LLMs seem to latch on to using it for that for some reason.
If anything, I'd assume the chatbots would use Indian English phrases like "do the needful" and those weird phrases that only make sense in Hindi but are translated to English.
This is easily fixed with the antislop sampler. https://github.com/sam-paech/antislop-sampler
The repetition of adjectives in adjacent sentences is much more of a tell than using the word delve, imo.
This is a good call-out, and one that might be fixable by setting a repetition penalty in generation — or better in training.
I recall in 1980's, style-analyzing software reporting word counts for words with high frequency in the text but low frequency in general usage - jargon. The suggestion was to re-word, and it was often a relevant clue to re-think by examining why one word was doing so much work. Detecting that sounds like more than repetition, but still feasible probabilistically and relevant at both usage and conceptual levels.
Eh, it’s not a _great_ one, because it’s also common in very bad human writing, particularly fiction.
Haha! You’re right. Maybe I’ll add a penalty for that and some other giveaway words and make a revision.