Sorry for asking such a general question but I need some guidance.
I know someone who writes grant applications professionally and has collected many winning proposals over the years. I was wondering if it’s possible to build a custom language model that learns from these documents to help generate new grant applications.
What would be the best approach for this? Are there any specific tools or frameworks that work well for training models on specialized writing like this? I’m looking for something that can understand the structure and language patterns used in successful grant proposals.
Any suggestions on where to start would be really helpful. Thanks!
I built something similar for my company’s internal docs about 2 years ago. Here’s what worked:
Fine-tune an existing model like GPT-3.5 or Llama instead of training from scratch. Way fewer documents needed and much better results.
Grant proposals are all about structure. Break those winning proposals into sections first - abstract, methodology, budget justification, etc. Train the model on each section separately.
Use Hugging Face Transformers library. It handles the heavy lifting and has solid documentation.
Here’s something I learned the hard way - grant writing has super specific formatting requirements and compliance language that changes by funding agency. Your training data needs examples from the same agencies you’re targeting.
Start smaller. Maybe just focus on generating strong abstracts or methodology sections before tackling entire proposals.
That person you know probably has goldmine data. Just make sure you’ve got proper permissions to use those documents for training.
data quality beats model architecture every time for this. grant writing’s niche, so clean those proposals hard - strip identifying info and fix formatting differences between years and agencies. I’d go with retrieval-augmented generation over fine-tuning since you probably don’t have thousands of examples.