echen

We're a human/AI data company (Surge AI) and work with many of the LLM companies to red team their systems. We actually just wrote up a blog post about it: https://www.surgehq.ai/blog/ai-red-teams-for-adversarial-tra...

Happy to answer any questions.

I've been experiencing the same. Here are a bunch of egregious spam mistakes we collected from different people to illustrate the problem: https://www.surgehq.ai/blog/are-the-spammers-winning-failure...

Agree that context is often needed! (Which is why it was strange to us that raters weren't presented with any context besides the comment text itself -- not even the subreddit, much less the original Reddit post.)

One interesting question, though: if "LETS FUCKING GOOOOO YOU DINGBAT" were meant to be a combative insult, would someone still add a bunch of O's ("GOOOOO" instead of merely "go")? My intuition is that if combativeness were intended, "let's fucking go, you dingbat" would be more likely than "LETS FUCKING GOOOOO YOU DINGBAT", but of course it's a bit hard to say without that context.

Gaming is a really fun and interesting labeling domain, given the community jargon (I'm actually a big Twitch user, but still couldn't tell you what many common emotes mean... took me years to understand "poggers") and context (is "i'm going to kill Garen" a death threat or in-game action?).

Great question! I'd love to measure that more rigorously too.

Although from what we've seen, the amount context sensitivity matters really depends on the labeling task / application.

For example, when you're trying to label a tweet that's a reply, context matters even more than when you're labeling a parent tweet: it's often hard to understand what the reply tweet is talking about when you can't see the full thread, it can be hard to tell whether something is a joke or an insult when you can't tell whether the replier and original tweeter follow each other or not, etc. This is important because sometimes our customers don't realize this, and will send us tweet text by itself instead of a full tweet link.

It's also important because even if your models are using text alone (and not a richer set of context/features), there may be patterns in the text itself that an ML could pick up on that a human wouldn't without that extra context.

We also have another post on context sensitivity if you're curious: https://www.surgehq.ai/blog/why-context-aware-datasets-are-c...

I'd love to chat. Want to reach out to the email in my profile? I'm the founder of a startup solving this exact problem (https://www.surgehq.ai), and previously built the human computation platforms at a couple FAANGs (precisely because this was a huge issue I always faced internally).

We work with a lot of the top AI/NLP companies and research labs, and do both the "typical" data labeling work (sentiment analysis, text categorization, etc), but also a lot more advanced stuff (e.g., search evaluation, training the new wave of large language models, adversarial labeling, etc -- so not just distinguishing cats and dogs, but rather making full use of the power of the human mind!).