u/Divine_Invictus

Hey all.

I recently started working on a project to improve machine-translated webnovels via style transfer. The basic idea is to take the clunky translated prose and rewrite it to something that reads like it was written by a professional author, while remaining as faithful as possible to the original text.

The source material is mostly amateur/MTL output full of direct sentence structure translations carried over from Chinese, awkward honorifics, over-translated idioms, that kind of thing. The goal isn't retranslation from the source but a cleanup of the English output.

The tricky part is I have no clean data pair for supervised approaches.

I've been looking at a few directions:

• Fine-tuning on target-style prose — collect high-quality English novels, fine-tune a small LLM to rewrite in that register.
• Just use a local LLM — run a local LLM and provide it with guidelines on what to rewrite and leave the same. No fine-tuning or anything needed, just hoping the transformer can handle it.

A few things I'm stuck on:

1. Is the faithfulness/fluency tradeoff actually manageable at the sentence level, or do I need paragraph-level context or more to preserve narrative coherence?
2. How do people handle domain-specific terms like

terminology

1. and catchphrase-type things that need to survive the rewrite unchanged? Hard constraints during decoding, or just hope the model learns to leave them alone?

Happy to hear about similar projects, relevant papers I might have missed, or just general lessons from working in this space. Thanks.

I’m looking for stories that have all of humanity is united against some sort of massive threat and the MC is one of their champions. This is as opposed to the MC being in it for solely for himself. Ideally this would be the driving conflict of the book rather than an arc or solely the endgame.

Bonus points if humanity has a bunch of powerful and competent champions and the MC isn’t the only one and if infighting is kept to a minimum.

Books like this I’ve read:
Shadow slave had all of humanity united against the encroaching dream realm
Reborn apocalypse has a bit more infighting, but it has a whole host of hyper competent champions and as of the latest book they have mostly been aligned.

Hey all, I'm working on a project to predict the chances of certain applicants being accepted to certain colleges based on their application portfolios and explaining what factors contribute the most and what changes would give the biggest advantage.

I have a relatively small dataset of about 1000 datapoints, which has academic stats like GPA, demographics, test scores, essays, extracurricular listings, awards, and more.

I'm currently using an LLM to extract features from the text-based fields like extracurriculars, awards, and essays. Basically, I give it a JSON to fill out where the LLM fills out certain fields, like average leadership score with a value between 0 and 5.

My first question is regarding the text fields. I was wondering if there are any better ways to extract explainable features from extracurriculars and activities? Because it needs to be explicable, I can't just use embedding vectors or something, so I've been struggling to come up with a better method.

After the data is processed, I just give it to a Catboost model with logloss loss and AUC eval. I currently achieve around 74% accuracy, but I'm looking to get it to around 80%. I was hoping for some tips on getting every bit of performance out of it. Additionally, I was wondering how I can tell if its even possible to get to 80%, given that college admissions is pretty messy. I don't want to spend to much time chasing an impossible goal.

I'm currently using the Shap Tree Explaner for explainability, which works pretty well, but I was wondering if there were any libraries to get cleaner graphics out of it.

Thanks for the help and feel free to ask clarifying questions