RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE, SQuAD, SuperGLUE and XNLI. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.
This paper presents a detailed replication study of the BERT pre-training model considering alternative design choices such as dynamic masking, removal of next sentence prediction loss, longer training time, larger batch sizes, additional training data, training on longer single document or cross-document sequences etc. to demonstrate their efficacy on several benchmark datasets and tasks by achieving the new state-of-the-art results. Overall, the paper is very well-written and the experimental setups are reasonable and thoroughly presented that would benefit the community for future research and exploration. However, I am not sure if the paper presents a case of adequate novelty in terms of ideas as many of them are rather obvious and the current state-of-the-art models could also improve considerably using similar experimental setups, which authors also acknowledged in footnote 2. Other comments: - Section 3.1: please clarify how exactly setting Beta2=0.98 would improve stability when training with large batch sizes. - It*s not clear what exactly was the motivation for proposing full-sentences and doc-sentences input formats. Please explain. - Although the presented metrics show that the removal of NSP loss helps, however, no explanation was provided based on qualitative evaluation as to why this is the case. Some task-specific examples would have been nice to discuss the effects of NSP loss. - Section 5: Please provide details on why you think the longest-trained model does not appear to overfit.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE, SQuAD, SuperGLUE and XNLI. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.
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 presents a detailed replication study of the BERT pre-training model considering alternative design choices such as dynamic masking, removal of next sentence prediction loss, longer training time, larger batch sizes, additional training data, training on longer single document or cross-document sequences etc. to demonstrate their efficacy on several benchmark datasets and tasks by achieving the new state-of-the-art results. Overall, the paper is very well-written and the experimental setups are reasonable and thoroughly presented that would benefit the community for future research and exploration. However, I am not sure if the paper presents a case of adequate novelty in terms of ideas as many of them are rather obvious and the current state-of-the-art models could also improve considerably using similar experimental setups, which authors also acknowledged in footnote 2. Other comments: - Section 3.1: please clarify how exactly setting Beta2=0.98 would improve stability when training with large batch sizes. - It*s not clear what exactly was the motivation for proposing full-sentences and doc-sentences input formats. Please explain. - Although the presented metrics show that the removal of NSP loss helps, however, no explanation was provided based on qualitative evaluation as to why this is the case. Some task-specific examples would have been nice to discuss the effects of NSP loss. - Section 5: Please provide details on why you think the longest-trained model does not appear to overfit.
assistant
This paper presents a detailed replication study of the BERT pre-training model considering alternative design choices such as dynamic masking, removal of next sentence prediction loss, longer training time, larger batch sizes, additional training data, training on longer single document or cross-document sequences etc. to demonstrate their efficacy on several benchmark datasets and tasks by achieving the new state-of-the-art results. Overall, the paper is very well-written and the experimental setups are reasonable and thoroughly presented that would benefit the community for future research and exploration. However, I am not sure if the paper presents a case of adequate novelty in terms of ideas as many of them are rather obvious and the current state-of-the-art models could also improve considerably using similar experimental setups, which authors also acknowledged in footnote 2. Other comments: - Section 3.1: please clarify how exactly setting Beta2=0.98 would improve stability when training with large batch sizes. - It*s not clear what exactly was the motivation for proposing full-sentences and doc-sentences input formats. Please explain. - Although the presented metrics show that the removal of NSP loss helps, however, no explanation was provided based on qualitative evaluation as to why this is the case. Some task-specific examples would have been nice to discuss the effects of NSP loss. - Section 5: Please provide details on why you think the longest-trained model does not appear to overfit.