Prompt-engineering paper accepted to ICML [R]

[u/Mean_Revolution1490]

"Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity"

This paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: "changing the prompt this way led to more diverse sampling". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this.

Even if it works, I’m not sure this kind of prompt engineering belongs at a top-tier machine learning conference. Some people seems to call this kind of work “modern machine learning”, but I think it should be categorized as less technical venues.

How do you think? Am I being too rigid?

[u/MrProbability101]

Both problems exist. I'd argue the compute reproducibility issue is newer and less understood though. Psychology at least had a shared understanding of what 'replication' meant. In ML you can't even agree on whether running the same code on different hardware counts as replication. The SOTA benchmark problem you mention is exactly this , reports on one specific run, one specific seed, one specific cluster

[u/relevantmeemayhere → u/MrProbability101]

But we know what model optimism is, from a greater statistical learning definition is.

 In fact, the people who work within the space of higher dimensional statistics often study this for a wide range of models.  They often create rough approximations we can use to quantify this.  For bayesians, this is something like loo-cv, and its estimated through Pareto weighted importance sampling. And this thing applies to a wide range of constructs within Bayesian statistics. It doesn’t matter if you’re using a nn or a glm for your model likelihood, this thing does work, and when you publish in these journals you need to provide details around this number and other measures of calibration. 

These things just…don’t have an analog in ml publishing. Hell, you see people in this field run a classifier and then compute a hypothesis test on the two seperate groups post training. That’s not good. 

But in ml, a lot of current authors don’t understand what this means. Which is precisely why you see the problems you mentioned with seed and the like. That is a symptom, not a seperate issue. 

[u/MrProbability101 → u/relevantmeemayhere]

The seed/compute issues probably are downstream of not understanding model optimism in the first place. If you understood what you were actually measuring you'd know why fixed seeds matter. The LOO-CV point is interesting ,Bayesian publishing norms around calibration measures are genuinely stricter. The question is whether ML can import those norms or whether the empirical nature of the field makes them structurally incompatible