Comment by scosman

I make a project for evals and fine-tuning and our default example task is a joke generator. It's a fun demo, but more importantly it's a really good use case to show how evaluating and optimizing LLMs is hard.

- There are a dozen plus common failure modes. How you split setup/punchline. Tropes. Toxicity. Template reuse. Each one needs a good eval.

- Datasets are hard: there's not much off the shelf, and as this author points out scraping gets a weird mix of quality.

- Models are really bad out of the box at humour.

At the end of the day it's just a hard problem that takes a lot of work and still isn't solved. GEPA prompts help, if you have good evals. Supervised fine-tuning works a little bit, but only if you training on a chain-of-thought thinking phase. We have a new evaluation builder that uses examples of edge cases for alignment, and jokes require the most iteration and feedback for refinement.

If you want to try it: https://github.com/kiln-ai/kiln