Hacker News Reader

Comment by mvelbaum | Hacker News Reader

By "debias" you obviously mean "bias in the direction of my particular worldview".

Yes. In my case, my particular worldview involves (i) indifferent to gender markers in text (I don't want my LLM to convert me to a female because I'm asking it to write a cover letter for a hairdresser/secretary/nurse role, for e.g.). I don't want my model to write it in AAVE if my name is a common african-american name. I don't want to get the best results only when I write as a middle-aged white man [1]. If I use the model for some decision making, I want it to be fair for subgroups [2], which is a reasonably objective metric [3].

[1] https://aclanthology.org/P19-1339.pdf [2] https://arxiv.org/pdf/1906.09208.pdf [3] https://developers.google.com/machine-learning/glossary/fair...

Will Grok really do any of those things? I would have guessed that RLHF would sort those things out even if it wasn't concerned with debiasing, but just about not making ridiculous mistakes.

These are what debiasing tasks are concerned with, more often than not. RLHF tuning depends greatly on the H part of it, and that data is probably proprietary. So, I guess time will tell. But if I were to hazard a guess based on the content of the announcement , I would say they couldn’t be bothered or couldn’t accomplish proper debiasing/rlhf tuning and therefore worded it so.

Isn't it enough to append "write it as if I'm a middle-aged white man" to your request to obtain the desired output?

No, people like him don't want certain queries to be available to anyone else.

The point I was trying to make was that certain NLP models (maybe these LLMs as well) might give better results if YOU speak to them as a middle aged white man.

To be very clear, I want ALL queries (I presume you mean LLM prompts) to be available to everyone.

Could you also explain what do you mean by people like me? Indians? NLP researchers? People in their thirties? Expatriates?

This just says more about how you think than OP.

Yeah, I'm just not gullible enough to be fooled by vague terms like "fairness" where whoever's in charge is going to decide what is fair and what isn't based on (most likely) some arbitrary worldview (which is most likely woke).

Actually, you just woefully misunderstand that objectivity, while never perfectly attainable, is indeed a metric, and that the bias of a model is inherently linked to the variety and quality of training data.

OP mentioned nothing about fairness; that's orthogonal to objectivity, and you're projecting your worldview onto OP's.

If you go through the links, specifically [3], you will find pretty objective definitions of multiple perspective of fairness. This is a mathematical concept.

In most cases, it's not an "arbitrary world view", it's "reality."

Reply on news.ycombinator.com