Mixed Setting Training Methods for Incremental Slot-Filling Tasks
Model training remains a dominant financial cost and time investment in machine learning applications. Developing and debugging models often involve iterative training, further exacerbating this issue. With growing interest in increasingly complex models, there is a need for techniques that help to reduce overall training effort. While incremental training can save substantial time and cost by training an existing model on a small subset of data, little work has explored policies for determining when incremental training provides adequate model performance versus full retraining. We provide a method-agnostic algorithm for deciding when to incrementally train versus fully train. We call this setting of non-deterministic full- or incremental training ``Mixed Setting Training*. Upon evaluation in slot-filling tasks, we find that this algorithm provides a bounded error, avoids catastrophic forgetting, and results in a significant speedup over a policy of always fully training.
This paper treats the important problem of when a model should be re-trained from scratch or incrementally trained as new data becomes available. Unfortunately, it is very hard to extract any useful conclusion out of this 6-pages paper, and I think considerable work is required before it can be useful for the community. The problem tackled is very important, but it is not very well formulated (e.g. their notation keeps changing along the paper, the assumptions are never properly stated, etc.) and their proposed solution is quite confusing, specially the “Reservoir sampling” part. Furthermore, the experimental section is extremely poor: the experiments performed are not well explained, the reported results are very weakly linked to their proposed method, there are no error bars what so ever in any of their single-curve plots, and even if we make an effort to understand which of the curves is their method, there doesn’t seem to be any substantial difference with respect to the other (baseline?). There are considerable typos throughout the paper, some of them considerably obscuring the meaning of their sentences. Just to report a few: - Fig 2 legend (which by the way is not referred anywhere in the text…) “A one-shot SGD settings as a function of ratio of initial to incremental data.” - Are “N” and “n” used interchangably in the notation? As all their notation, we encourage them to fix it and define it clearly. - “The reservoir of samples is inferred before a training epoch”. Do they mean sampled, or that they run the inference of their model on all the data-points in the reservoir? Other misc comments: - Instead of saying that your model is characterized by a weight matrix W, you should say “the weights of our model” or the “parameters of the model”. In modern Machine Learning this might be much more than a single weight matrix. - It is very hard to understand the utility of the reservoir sampling, and there should be a comparison to other simpler ways of sampling from the available data (like uniformly sampling n elements). No ablation studies what so ever are performed.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Mixed Setting Training Methods for Incremental Slot-Filling Tasks
Model training remains a dominant financial cost and time investment in machine learning applications. Developing and debugging models often involve iterative training, further exacerbating this issue. With growing interest in increasingly complex models, there is a need for techniques that help to reduce overall training effort. While incremental training can save substantial time and cost by training an existing model on a small subset of data, little work has explored policies for determining when incremental training provides adequate model performance versus full retraining. We provide a method-agnostic algorithm for deciding when to incrementally train versus fully train. We call this setting of non-deterministic full- or incremental training ``Mixed Setting Training*. Upon evaluation in slot-filling tasks, we find that this algorithm provides a bounded error, avoids catastrophic forgetting, and results in a significant speedup over a policy of always fully training.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper treats the important problem of when a model should be re-trained from scratch or incrementally trained as new data becomes available. Unfortunately, it is very hard to extract any useful conclusion out of this 6-pages paper, and I think considerable work is required before it can be useful for the community. The problem tackled is very important, but it is not very well formulated (e.g. their notation keeps changing along the paper, the assumptions are never properly stated, etc.) and their proposed solution is quite confusing, specially the “Reservoir sampling” part. Furthermore, the experimental section is extremely poor: the experiments performed are not well explained, the reported results are very weakly linked to their proposed method, there are no error bars what so ever in any of their single-curve plots, and even if we make an effort to understand which of the curves is their method, there doesn’t seem to be any substantial difference with respect to the other (baseline?). There are considerable typos throughout the paper, some of them considerably obscuring the meaning of their sentences. Just to report a few: - Fig 2 legend (which by the way is not referred anywhere in the text…) “A one-shot SGD settings as a function of ratio of initial to incremental data.” - Are “N” and “n” used interchangably in the notation? As all their notation, we encourage them to fix it and define it clearly. - “The reservoir of samples is inferred before a training epoch”. Do they mean sampled, or that they run the inference of their model on all the data-points in the reservoir? Other misc comments: - Instead of saying that your model is characterized by a weight matrix W, you should say “the weights of our model” or the “parameters of the model”. In modern Machine Learning this might be much more than a single weight matrix. - It is very hard to understand the utility of the reservoir sampling, and there should be a comparison to other simpler ways of sampling from the available data (like uniformly sampling n elements). No ablation studies what so ever are performed.
assistant
This paper treats the important problem of when a model should be re-trained from scratch or incrementally trained as new data becomes available. Unfortunately, it is very hard to extract any useful conclusion out of this 6-pages paper, and I think considerable work is required before it can be useful for the community. The problem tackled is very important, but it is not very well formulated (e.g. their notation keeps changing along the paper, the assumptions are never properly stated, etc.) and their proposed solution is quite confusing, specially the “Reservoir sampling” part. Furthermore, the experimental section is extremely poor: the experiments performed are not well explained, the reported results are very weakly linked to their proposed method, there are no error bars what so ever in any of their single-curve plots, and even if we make an effort to understand which of the curves is their method, there doesn’t seem to be any substantial difference with respect to the other (baseline?). There are considerable typos throughout the paper, some of them considerably obscuring the meaning of their sentences. Just to report a few: - Fig 2 legend (which by the way is not referred anywhere in the text…) “A one-shot SGD settings as a function of ratio of initial to incremental data.” - Are “N” and “n” used interchangably in the notation? As all their notation, we encourage them to fix it and define it clearly. - “The reservoir of samples is inferred before a training epoch”. Do they mean sampled, or that they run the inference of their model on all the data-points in the reservoir? Other misc comments: - Instead of saying that your model is characterized by a weight matrix W, you should say “the weights of our model” or the “parameters of the model”. In modern Machine Learning this might be much more than a single weight matrix. - It is very hard to understand the utility of the reservoir sampling, and there should be a comparison to other simpler ways of sampling from the available data (like uniformly sampling n elements). No ablation studies what so ever are performed.