Initial README.md for AD-Word

Initial description of Ad-Word dataset.

README.md

@@ -20,4 +20,148 @@ dataset_info:
     num_examples: 57989
   download_size: 3514148
   dataset_size: 11868266
+language:
+- en
+tags:
+- diagnostic
+- perturbation
+- homoglyphs
+pretty_name: Ad-Word
+size_categories:
+- 100K<n<1M
 ---
+# Ad-Word Dataset
+
+The Ad-Word dataset contains adversarial word perturbations created using 9 different attack strategies, organized into three classes: phonetic, typo, and visual attacks. The dataset, introduced in ["Close or Cloze? Assessing the Robustness of Large Language Models to Adversarial Perturbations via Word Recovery"](https://aclanthology.org/2025.coling-main.467), contains 7,911 words perturbed multiple times with each attack strategy, creating 327,382 pairs of clean and perturbed words organized by attack.
+
+## Dataset Construction
+
+The base vocabulary was constructed from the most frequent 10,000 words in the Trillion Word Corpus, excluding words shorter than four characters. Finally, the dataset was augmented with:
+- 250 uncommon English words added to the test set
+- 100 common English borrowed words that are frequently stylized with accents (50 in train, 25 in test, 25 in validation)
+
+These additions were sampled from the Wikitext corpus (`wikitext-103-v1`) to help bound the performance of models that ignore non-ASCII characters or use limited dictionaries.
+
+## Attack Strategies
+
+The perturbations are organized into three classes.
+The classes are organized by what information they are meant to **preserve**.
+For instance, visual attacks use homoglyphs that are visually similar, but may not preserve phonetic similarity if rendered phonetically.
+
+1. Phonetic Attacks
+   - ANTHRO Phonetic [Le et al., 2022]
+   - PhoneE (introduced in Moffett and Dhingra, 2025)
+   - Zeroé Phonetic [Eger and Benz, 2020]
+
+2. Typo Attacks
+   - ANTHRO Typo [Le et al., 2022]
+   - Zeroé Noise [Eger and Benz, 2020]
+   - Zeroé Typo [Eger and Benz, 2020]
+
+3. Visual Attacks
+   - DCES [Eger et al., 2019]
+   - ICES [Eger et al., 2019]
+   - LEGIT [Seth et al., 2023]
+
+## Per-Attack Unique Clean-Perturbed Pairs
+
+| Attack Class | Attack Name | Train | Valid | Test |
+|------------|-------------|--------|--------|------|
+| phonetic | anthro_phonetic | 17,649	|	4,098	|	4,787 |
+| phonetic | phonee        | 24,339	|	5,551	|	6,439 |
+| phonetic | zeroe_phonetic | 28,562	|	6,514	|	7,468 |
+| typo |    anthro_typo    | 15,437	|	3,587	|	4,137 |
+| typo |    zeroe_noise    | 27,079	|	6,233	|	7,173 |
+| typo |    zeroe_typo     | 19,912	|	4,721	|	5,314 |
+| visual |  dces           | 28,722	|	6,625	|	7,560 |
+| visual |  ices           | 29,324	|	6,762	|	7,713 |
+| visual |  legit          | 27,796	|	6,481	|	7,398 |
+
+## Dataset Structure
+
+The dataset contains the following columns:
+- `clean`: The original word
+- `perturbed`: The perturbed version of the word
+- `attack`: The attack strategy used to perturb the words
+
+The dataset is split into `train`/`valid`/`test` splits, with each split containing an indepedent set of words perturbations from all attack strategies.
+There are 5,131 unique **clean** words in the `train` split, 1,214 in the `valid` split, and 1,584 in the `test` split.
+
+## Usage Example
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import random
+
+adword = load_dataset("lmoffett/ad-word")
+
+model_name = "facebook/opt-125m"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+samples = random.sample(list(adword['test']), 3)
+
+# Test recovery
+for sample in samples:
+    # This is not a tuned prompt, just a simple example
+    prompt = f"""This word has a typo in it. Can you figure out what the original word was?
+    Word with typo: "{sample['perturbed']}"
+    Oh, "{sample['perturbed']}" is a misspelling of the word \""""
+    
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(**inputs, max_new_tokens=5)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    print('-' * 60)
+    print(f"{sample['clean']} -> {sample['perturbed']}")
+    print(f"{response}")
+```
+
+## References
+
+- [Le et al., 2022] Le, Thai, et al. "Perturbations in the wild: Leveraging human-written text perturbations for realistic adversarial attack and defense." arXiv preprint arXiv:2203.10346 (2022).
+- [Eger and Benz, 2020] Eger, Steffen, and Yannik Benz. "From hero to zéroe: A benchmark of low-level adversarial attacks." Proceedings of the 1st conference of the Asia-Pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing. 2020.
+- [Eger et al., 2019] Eger, Steffen, et al. "Text processing like humans do: Visually attacking and shielding NLP systems." arXiv preprint arXiv:1903.11508 (2019).
+- [Seth et al., 2023] Seth, Dev, et al. "Learning the Legibility of Visual Text Perturbations." arXiv preprint arXiv:2303.05077 (2023).
+
+## Related Resources
+
+- Cloze or Close Code Repository (including PhoneE): [GitHub](https://github.com/lmoffett/cloze-or-close)
+- LEGIT Dataset: [HuggingFace](https://huggingface.co/datasets/dvsth/LEGIT)
+- Zeroé Repository: [GitHub](https://github.com/yannikbenz/zeroe)
+- ANTHRO Repository: [GitHub](https://github.com/lethaiq/perturbations-in-the-wild)
+
+## Version History
+
+### v1.0 (January 2025)
+- Initial release of the AdWord dataset
+- Set of perturbations from 9 attack strategies
+- Train/valid/test splits with unique clean-perturbed pairs
+
+## License
+
+This dataset is licensed under Apache 2.0.
+
+## Citation
+
+If you use this dataset in your research, please the original paper:
+
+```bibtex
+@inproceedings{moffett-dhingra-2025-close,
+    title = "Close or Cloze? Assessing the Robustness of Large Language Models to Adversarial Perturbations via Word Recovery",
+    author = "Moffett, Luke and Dhingra, Bhuwan",
+    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
+    year = "2025",
+    publisher = "Association for Computational Linguistics",
+    pages = "6999--7019"
+}
+```
+
+## Limitations
+
+There is no definitive measurement of the effectiveness of these attacks.
+The original paper provides human baselines, but there are many factors that effect the recoverability of perturbated words.
+When applying these attacks to new problems, researchers should ensure that the attacks align with their expections.
+For instance, the ANTHRO attacks are sourced from public internet corpora.
+In some cases, there are very few attacks for a given word, and, in many cases, those attacks only involve casing changes.