zlacker

>>belter+(OP)
Eh, their argument is simply that they tuned temperature settings to encourage the model to output slight variations on memorized data. But this is kind of just one of many things you do with a language model and certainly doesn’t imply intent to avoid copyright allegations.

Just implies they tuned it for user experience.

I was expecting there to be some discovery around them deliberately fine tuning their model to output modifications if and only if the code had a certain license.

>>Shamel+ba
What's the value of slight variations? Isn't it more likely that the memorized data was already known to be good and effective? It doesn't seem like a useful change unless your goal is to avoid infringement. I don't see how randomly permuting the suggestions improves UX.

>>keving+4E
The lowest temperature isn't always the one that results in working code! This was shown in the original Codex paper:

> When evaluating pass@k, it is important to optimize sampling temperature for the particular value of k. In Figure 5, we plot pass@k against the number of samples k and the sampling temperature. We find that higher temperatures are optimal for larger k, because the resulting set of samples has higher diversity, and the metric rewards only whether the model generates any correct solution.

> In particular, for a 679M parameter model, the optimal temperature for pass@1 is T∗ = 0.2 and the optimal temperature for pass@100 is T∗ = 0.8. With these temperatures, we find that pass@1 and pass@100 scale smoothly as a function of model size (Figure 6).

So even with pass@1 (likelihood of getting the right answer in 1 attempt) you don't use T=0, so there will be slight variations in the output each time.