| --- |
| license: mit |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: val |
| path: data/val-* |
| - split: val_dense |
| path: data/val_dense-* |
| - split: val_sparse |
| path: data/val_sparse-* |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 825600000 |
| num_examples: 1600000 |
| - name: val |
| num_bytes: 8256000 |
| num_examples: 16000 |
| - name: val_dense |
| num_bytes: 2064000 |
| num_examples: 4000 |
| - name: val_sparse |
| num_bytes: 82560000 |
| num_examples: 160000 |
| download_size: 354675733 |
| dataset_size: 918480000 |
| --- |
| |
| Data for [**Flip-Flop Language Modeling**](https://arxiv.org/abs/2306.00946). The task is to correctly execute the sequential operations of a 1-bit register. The Transformer architecture, despite being apparently built for this operation, makes sporadic extrapolation errors (*attention glitches*). An open challenge is to fix these without recourse to long-tailed data or a recurrent architecture. Splits reflect the FFLM setup from the paper: |
| - `train`: 1.6M sequences from FFL(0.8) *(256 instructions, 80% ignore, 10% read, 10% write)*. |
| - `val`: 16K sequences from FFL(0.8). |
| - `val_dense`: 4K sequences from FFL(0.1). |
| - `val_sparse`: 160K sequences from FFL(0.98). |
|
|
| Usage |
| --- |
| ```python |
| import torch |
| import datasets |
| |
| dataset = datasets.load_dataset('synthseq/flipflop') |
| dataset['train'][0] # {'text': 'w1i1w0i0 ... |
| |
| def tokenize_batch(batch): |
| mapping = {'w': 0, 'r': 1, 'i': 2, '0': 3, '1': 4} |
| tokenized_batch = [[mapping[char] for char in s] for s in batch['text']] |
| return {'tokens': torch.tensor(tokenized_batch, dtype=torch.int64)} |
| |
| dataset.set_transform(tokenize_batch) |
| dataset['train'][0] # {'tokens': tensor([0, 4, 2, 4, 0, 3, 2, 3, 2 ... |
| ``` |
|
|
| Citation |
| --- |
|
|
| ``` |
| @article{liu2023exposing, |
| title={Exposing Attention Glitches with Flip-Flop Language Modeling}, |
| author={Liu, Bingbin and Ash, Jordan T and Goel, Surbhi and Krishnamurthy, Akshay and Zhang, Cyril}, |
| journal={arXiv preprint arXiv:2306.00946}, |
| year={2023} |
| } |
| ``` |
