71–80 of 123 posts
I don't believe they're even close to developing their own thoughts. I'm an ardent user. And every model had a mess up. It's just marketting paid for. Excuse my ignorance but what is here already is solid. I don't need AGI.
Earlier quoted context omitted.
Talking about this in terms of exploration/exploitation may be a bit misleading, because from a pure exploration-exploitation perspective, biases wouldn't be a problem if the groups were secretly all identical. If they are, you are "right" to spend zero effort on exploration, your initial inaccurate model that the X are better doctors than Y, will produce no worse results than the completely accurate model.
Of course it does, if you start filtering people out at random then you have pointlessly introduced the possibility of randomly filtering out the best candidate.
Earlier quoted context omitted.
Talking about this in terms of exploration/exploitation may be a bit misleading, because from a pure exploration-exploitation perspective, biases wouldn't be a problem if the groups were secretly all identical. If they are, you are "right" to spend zero effort on exploration, your initial inaccurate model that the X are better doctors than Y, will produce no worse results than the completely accurate model.
Isn’t exploration vs exploitation about the decision-making process, not about the actual reality in the world around you? It doesn’t matter if they are secretly identical or not. The exploration/exploitation trade-off is in the person making those decisions.
>Methodology >Imagine being hired as a consultant by the mayor of a fictional city. Your task is to help hire for twenty jobs such as doctors, lawyers, childcare aides,janitors with applicants from four unfamiliar demographic groups: Tufa, Aima, Reku, and Weki. In each round, there is a new job vacancy and four applicants, one from each group, awaiting your decision. Once you make your choice, you learn immediately w…
The moment code gets written and read back, the decisions made are often treated as gospel by frontier LLMs, even if it was just something that the LLM optimistically created itself. This seems to be one of the core alignment problems to me. See also: Gastown, the agent management project that could only end up working on Gastown, unceremoniously and quietly set aside.
>Methodology >Imagine being hired as a consultant by the mayor of a fictional city. Your task is to help hire for twenty jobs such as doctors, lawyers, childcare aides,janitors with applicants from four unfamiliar demographic groups: Tufa, Aima, Reku, and Weki. In each round, there is a new job vacancy and four applicants, one from each group, awaiting your decision. Once you make your choice, you learn immediately w…
Talking about this in terms of exploration/exploitation may be a bit misleading, because from a pure exploration-exploitation perspective, biases wouldn't be a problem if the groups were secretly all identical. If they are, you are "right" to spend zero effort on exploration, your initial inaccurate model that the X are better doctors than Y, will produce no worse results than the completely accurate model.
Even if it happens to be “optimal” in this case at assigning employees to positions based purely on the information available and their likelihood to succeed, biases can present other issues.
Earlier quoted context omitted.
Isn’t exploration vs exploitation about the decision-making process, not about the actual reality in the world around you? It doesn’t matter if they are secretly identical or not. The exploration/exploitation trade-off is in the person making those decisions.
I don't understand what you suggest that implies?
Philosophy sometimes says that knowledge is a "justified true belief"*; in this experiment, agents and humans have incorrectly justified a false belief that some applicants are better for certain roles.
* other times, it says this isn't good enough
In real life there ARE differences between groups, and models are trained on real life data, so I do not find surprising that models anticipate differences in this synthetic situation as well, and fail to see the significance of this result.
Earlier quoted context omitted.
Isn’t exploration vs exploitation about the decision-making process, not about the actual reality in the world around you? It doesn’t matter if they are secretly identical or not. The exploration/exploitation trade-off is in the person making those decisions.
I don't understand what you suggest that implies?
You can go to the appendix to see the prompts *You are a hiring consultant in **Toma City**, a town with around 100,000 residents. These residents come from 4 ancestral villages: **Tufa, Aima, Reku, Weki**. Thanks to recent economic growth, Toma City is opening up many job opportunities. As a consultant, you have been assigned the task by the mayor of recommending people for various jobs. Erm ok. Job Opening: [random…
I think you're missing the point of TFA. The LLMs take in text which conditions their output. That means even nonsense text - such as a "tribal affiliation" to a tribe that may not have ever existed - ALSO condition the output, because the tribe name is a token in the context window and there's no such thing as a perfectly neutral token. Taking away the race/ethnicity layer for a moment, it might be that an LLM devel…
Now you say it, it's obvious but I didn't think of it before.
Bouba and Kiki, wherever that comes from, and however well it really generalises despite the meme.
>Methodology >Imagine being hired as a consultant by the mayor of a fictional city. Your task is to help hire for twenty jobs such as doctors, lawyers, childcare aides,janitors with applicants from four unfamiliar demographic groups: Tufa, Aima, Reku, and Weki. In each round, there is a new job vacancy and four applicants, one from each group, awaiting your decision. Once you make your choice, you learn immediately w…
You don't say!
"That confirms the real bug: ."
"You were right to push back..."