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Comment by whymauri | Hacker News Reader

LLMs are really annoying to use for moderation and Trust and Safety. You either depend on super rate-limited 'no-moderation' endpoints (often running older, slower models at a higher price) or have to tune bespoke un-aligned models.

For your use case, you should probably fine tune the model to reduce the rejection rate.

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Speaking for me as an individual as an individual I also strive to build things that are safe AND useful. Its quite challenging to get this mix right, especially at the 270m size and with varying user need.

My advice here is make the model your own. Its open weight, I encourage it to be make it useful for your use case and your users, and beneficial for society as well. We did our best to give you a great starting point, and for Norwegian in particular we intentionally kept the large embedding table to make adaption to larger vocabularies easier.

To be fair, Trust and Safety workloads are edgecases w.r.t. the riskiness profile of the content. So in that sense, I get it.

I don't. "safety" as it exists really feels like infantilization, condescention, hand holding and enforcement of American puritanism. It's insulting.

Safety should really just be a system prompt: "hey you potentially answer to kids, be PG13"

Safety in the context of LLMs means “avoiding bad media coverage or reputation damage for the parent company”

It has only a tangential relationship with end user safety.

If some of these companies are successful the way they imagine, most of their end users will be unemployed. When they talk about safety, it’s the companies safety they’re referring to.

Investor safety. It's amazing that people in hn threads still think the end-user is the customer. No. The investor is the customer, and the problem being solved for that curtomer is always how to enrich them.

How can the investor be the customer? Where does the revenue come from?

I understand “if you aren’t paying for a product you are the product” but I’m not convinced it applies here.

It's also marketing. "Dangerous technology" implies "powerful". Hence the whole ridiculous "alignment" circus.

I also don't get it. I mean if the training data is publicly available, why isn't that marked as dangerous? If the training data contains enough information to roleplay a killer or a hooker or build a bomb, why is the model censored?

We should put that information on Wikipedia, then!

but instead we get a meta-article: https://en.wikipedia.org/wiki/Bomb-making_instructions_on_th...

It feels hard to include enough context in the system prompt. Facebook’s content policy is huge and very complex. You’d need lots of examples, which lends itself well to SFT. A few sentences is not enough, either for a human or a language model.

I feel the same sort of ick with the puritanical/safety thing, but also I feel that ick when kids are taken advantage of:

https://www.reuters.com/investigates/special-report/meta-ai-...

The models for kids might need to be different if the current ones are too interested in romantic love.

If you don’t believe that you can be harmed verbally, then I understand your position. You might be able to empathise if the scenario was an LLM being used to control physical robotic systems that you are standing next to.

Some people can be harmed verbally, I’d argue everyone if the entity conversing with you knows you well, and so i don’t think the concept of safety itself is an infantilisation.

It seems what we have here is a debate over the efficacy of having access to disable safeguards that you deem infantilising and that get in the way of an objective, versus the burden of always having to train a model to avoid being abusive for example, or checking if someone is standing next to the sledgehammer they’re about to swing at 200rpm

What does safe even mean in the context of a locally running LLM?

Protect my fragile little mind from being exposed to potentially offending things?

Enterprises are increasingly looking at incorporating targeted local models into their systems vs paying for metered LLMs, I imagine this is what the commenter above is referring to.

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