Some examples of ablation studies: 
- No NSP: A bidirectional model which is still trained using the “Masked LM” but doesn’t include the “next sentence prediction” (NSP) task
- LTR and No NSP: Same as above but unidirectional, reads left to right and doesn’t use masking
- Trying to understand the effect of model size by building versions of BERT with varying numbers of layers, hidden units, etc but using the same hyperparameters and training procedure on each

BERT Ablation Studies Examples

- A task-specific model architecture is added to improve the representation of the tasks.
- Running many experiments with cheaper models after pre-computing the expensive representation of the training data once is beneficial for the computation.


Benefits of Feature-based Approach

Ablation studies on BERT are attempts to understand the relative importance of the various pieces of BERT’s functionality by testing them in isolation.

University of Massachusetts at Amherst

This paper presents BERT fine-tuning results on 11 Natural Language Processing (NLP) tasks.

BERT Experiments

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

https://paperswithcode.com/method/bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

The GLUE benchmark is a collection of diverse natural language understanding tasks.

General Language Understanding Evaluation (GLUE)

The SQuAD v1.1 is a collection of 100k crowdsourced question/answer pairs.

Stanford Question Answering Dataset 
(SQuAD v1.1)

The SQuAD 2.0 task extends the SQuAD 1.1 problem definition by allowing for the possibility that no short answer exists in the provided paragraph, making the problem more realistic.

Stanford Question Answering Dataset 
(SQuAD v2.0) 

The SWAG dataset contains 113k sentence-pair completion examples that evaluate grounded common-sense inference.

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