metadata
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. 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
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}
}