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.

> 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 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.

https://arxiv.org/abs/2409.01754

https://youtu.be/sy4SwW0QkoA

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.

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.

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

Realistically NPCs should probably respond with increasing urgency if you forget their quest, and eventually give up on you.

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