[dominicwhyte]

It outputs both - { isLikelySpam: boolean, reason: string }

Then we have an inbox app (also made in Retool) that our support team uses to manually review any submissions that are isLikelySpam = true. The <reason> helps to understand why it was flagged.

Our use case is for a form builder (https://fillout.com) but I imagine this type of use case is pretty common for any app that has user-generated content

[hereonout2]

I'm interested in this aspect of llms too, are you simply just passing it some input (email, customer message) and asking chat gpt to decide if it's spam? Do you provide any prior examples of spam / not spam or just rely on the knowledge already embedded within the model?

Spam detection is a classic example for classification problems. I guess I'm trying to gauge whether there's an entire suite of traditional problems that llms solve well enough by simply asking a question of the base model. I've found a few areas in my own work where this is the case.

[dominicwhyte]

We give 2-3 examples and find that it works pretty well (few shot fine tuning) but haven't tried actual fine tuning yet so I don't have a 1-1 comparison.

We also have other spam filters that are not LLM-based. One of the main benefits of the LLM-based approach is that it's good at catching people who try to avoid detection (e.g. someone purposefully mis-spelling suspicious words like "pa$$word")