I'm interested in the economics of computing and big-picture trends in machine learning. https://www.tamaybesiroglu.com/

Wiki Contributions


If the data is low-quality and easily distinguishable from human-generated text, it should be simple to train a classifier to spot LM-generated text and exclude this from the training set. If it's not possible to distinguish, then it should be high-enough quality so that including it is not a problem.

Good question. Some thoughts on why do this:

  • Our results suggest we won't be caught off-guard by highly capable models that were trained for years in secret, which seems strategically relevant for those concerned with risks
  • We looked whether there was any 'alpha' in these results by investigating the training durations of ML training runs, and found that models are typically trained for durations that aren't far off from what our analysis suggests might be optimal (see a snapshot of the data here)
  • It independently seems highly likely that large training runs would already be optimized in this dimension, which further suggests that this has little to no action-relevance for advancing the frontier

I'm not sure what you mean; I'm not looking at log-odds. Maybe the correlation is an artefact from noise being amplified in log-space (I'm not sure), but it's not obvious to me that this isn't the correct way to analyse the data.

Thanks! At least for Gopher, if you look at correlations between reductions in log-error (which I think is the scaling laws literature suggests would be the more natural framing) you find a more tighter relationship, particularly when looking at the relatively smaller models.

Thanks, though I was hoping for something like a Google Sheet containing the data.

This is super interesting. Are you able to share the underlying data?

It is unless it's clear that a side that made a mistake in entering a lopsided bet. I guess the rule-of-thumb is to follow big bets (which tends to be less clearly lopsided) or bets made by two people whose judgment you trust.

Are you thinking of requiring each party to accept bets on either side?

Being forced to bet both sides could ensure honesty, assuming they haven't found other bets on the same or highly correlated outcomes they can use for arbitrage.

Yes. Good point.

And including from other parties, or only with each other?

I was thinking that betting would be restricted to the initial two parties (i.e. A and B), but I can imagine an alternative in which it's unrestricted.

You could imagine one party was betting at odds they consider very favourable to them, and the other party betting at odds they consider only slightly favourable, based on their respective beliefs. Then, even if they don't change their credences, one party has more room to move their odds towards their own true credences, and so drag the average towards it, and take the intermediate payments,

Sorry, I'm confused. Isn't the 'problem' that the bettor who takes a relatively more favourable odds has higher expected returns a problem with betting in general?

We also propose betting using a mechanism that mitigates some of these issues:

Since we recognize that betting incentives can be weak over long time-horizons, we are also offering the option of employing Tamay’s recently described betting procedure in which we would enter a series of repeated 2-year contracts until the resolution date.

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