We have 2 binary datasets in there [1], but of course, those results are not separately presented - you see the averages. The choice of datasets here is based on what other papers report, to have comparable results. In our experience with real data, AL doesn't fare any better on average - our pipelines use different representations (not just BERT based, includes some legacy ones like n-grams+tf-idf) and classifiers.
We do see some strong peformance with RoBERTa (Fig 6) and the margin strategy, which is an uncertainty based strategy like least confidence (Fig 7) - these probably correspond to your observations (to an extent).
It is a little hard to comment on differences without looking closely (I'd be happy to go through any document/tech. report you might be able to share) but in our experience, we have seen inconsistencies arise from:
* Not performing model selection at each iteration.
* Not calibrating the selected model at each iteration.
In these cases, a non-random query strategy sometimes just makes up for the lack of proper model selection, making it seem like the query strategy is stronger than it really is. Another point of difference is the seed data size - that can have a significant effect on "priming" the query strategy.
On a related note, specifically wrt uncertainty-based strategies, sampling bias is a known problem [2].
[1] On the Fragility of Active Learning, https://arxiv.org/abs/2403.15744
[2] Section 1.2 in Two faces of active learning, https://cseweb.ucsd.edu/~dasgupta/papers/twoface.pdf
* Understood that dataset selection exists within the context of what others are doing and trying to provide comparable results. That being said, binary sentiment classifiers are (and should be) a pretty low bar for any reasonable technique in 2024, so it necessarily makes the reader wonder whether the conclusions are actually applicable to problems they might be facing. The point is taken that you're also looking at some older methods like TF-IDF which are prevalent in the (historical) AL literature.
* Performing model selection and calibration (cross-validation) at each iteration makes intuitive sense, but doesn't really make practical sense. If the goal of AL in the real world is getting frequent updates from a human oracle, new models need to be trained in seconds (at absolute maximum). Even if performance improves by spending time doing model selection/CV, humans simply won't tolerate waiting around for new responses to label.
* In your first link, the sample sizes seem extremely...small? If I was consistently faced with datasets only containing 1000 or 2000 data points, I'd probably just do supervised learning because the effort to set up a good AL pipeline (not to mention dealing with stopping criteria etc.) probably wouldn't be worth it. My experience has mostly been with datasets containing tens of thousands of data points (if not more).
* 100% agree that sampling bias is a huge issue with a number of AL strategies and one we worked on extensively. A component of that is trying to design the interaction such that the human labeler has some insight into parts of the data distribution that the model is making assumptions about; your second link is very nice and we tried a number of clustering-type techniques alongside our active learner to try and offset sample bias. More generally, I think this points to the importance of considering any human-in-the-loop ML problem in a way that takes into account more than just algorithmic efficiency.
* Reg. model selection/calibration: I acknowledge that in interactive setups cross-validation (CV) is not practical, but that is a particular kind of model selection. And faster alternatives exist for specific model families, such as approx. gradients [1,2], out-of-bag error (Random Forests), Infinitesimal Jacknife [3]. The bigger problem to me (for no model selection) is that there is a philosophical gap here: why would we fit the model with a default parameter blessed by a specific library? For ex., for LinearSVC in scikit, what is so great about C=1? And then, papers should mention that their query strategies are evaluated against C=1, but that of course doesn't sound as cool :-)
Problem #2 is what I had mentioned earlier: the AL query strategy seems to be doing two things now - makes up for the lack of good hyperparameters in the current model, and picking up "good" points - how do I know which it does more of? Or what is it really good at? If its performance is influenced by a lot of the former, it is hardly surprising that when you actually perform model selection, the gap between a query strategy and random decreases.
I guess my point is it doesn't have to be CV - but there needs to be model selection, because there is no well defined notion of a "default model". Calibration is less of an issue, because you can do this just once on the best model, and its not as expensive.
* In that paper [4], the effect of AL is shown for upto 5k points, but the unlabelled pool size is 20k points ("Other Settings" in Page 5). So we do require good instance selection.
On a different note, my hunch is that as language models get better at holding a broad variety of concept information, and it keeps getting easier to adapt them to specific tasks with just few examples/shots, we'll see whatever benefit AL has over random diminish. First (the bigger reason), the number of examples required to get good performance would reduce to the point that it might not make practical sense to set up an AL pipeline. And second, it would get easier to adapt the model per se, so that the difference (in terms of final model accuracy) between k good points and k randomly selected points might not be large. I guess we already see this in some form with SetFit [5] where examples are randomly picked to reach a good accuracy. I wonder if you have a view on that.
[1] Hyperparameter optimization with approximate gradient, https://proceedings.mlr.press/v48/pedregosa16.html
[2] Optimizing Millions of Hyperparameters by Implicit Differentiation, https://proceedings.mlr.press/v108/lorraine20a.html
[3] A Swiss Army Infinitesimal Jackknife, https://proceedings.mlr.press/v89/giordano19a.html
[4] On the Fragility of Active Learners, https://arxiv.org/pdf/2403.15744.pdf
[5] Efficient Few-Shot Learning Without Prompts, https://neurips2022-enlsp.github.io/papers/paper_17.pdf
I understand the view that from a scientific perspective it is difficult to quantify the utility of a technique if it is providing multiple benefits at the same time. Not necessarily saying that's what's happening here, but it's worth pointing out that you can also look at this from a practical angle.For any given dataset your chosen model and set of hyperparameters probably isn't optimal, so if indeed AL is compensating for suboptimal choices in model selection, that may still be valuable.
Another potentially interesting angle to consider is label imbalance; because we were constructing binary classification problems out of multiclass problems (as discussed earlier), some evidence shows AL strategies outperform random sampling to a significantly greater degree in imbalanced scenarios [1].
I agree with you that we're moving towards a world in which transformer models are so good that zero-shot or few-shot approaches to classification are going to be the way to go. Stuff I did 3 years ago I probably would not do today.
Right now we are somewhere in between where the results are presented to seem somewhat generally useful, but when you tweak the hyperparams you realize that the utility is potentially quite narrow.
We either need broad utility or increased rigor in reporting - we cannot abandon both.
Yeah that's an interesting paper [1], and label imbalance is a case we have not investigated (we were able to reproduce some results from it - the DAL experiments in Section A.3 "Reproducibility Experiments" in [2] come from there).
[1] Active Learning for BERT: An Empirical Study, https://aclanthology.org/2020.emnlp-main.638.pdf
[2] One the Fragility of Active Learners, https://arxiv.org/abs/2403.15744
In our cases, < 100 labels on modern models (per task) goes far. We can do better, but more useful problems to solve. Impressive how far these have come!