Datasets:
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license: other
license_name: fair-use-research
language:
- en
tags:
- spelling-bee
- word-games
- human-difficulty
- orthographic-constraints
- llm-evaluation
- benchmark
size_categories:
- n<1K
task_categories:
- text-generation
pretty_name: NYT Spelling Bee Human Difficulty
dataset_info:
features:
- name: date
dtype: string
- name: center_letter
dtype: string
- name: outer_letters
sequence: string
- name: answer_words
sequence: string
- name: answer_user_counts
sequence: int32
splits:
- name: train
num_examples: 58
---
# NYT Spelling Bee — Human Difficulty Dataset
Human solve-frequency data for 58 New York Times Spelling Bee puzzles (June–July 2025), sampled from 10,000 users per puzzle. Serves as ground truth for evaluating LLM orthographic constraint satisfaction.
| Stat | Value |
|------|-------|
| Puzzles | 58 |
| Date range | 2025-06-02 to 2025-07-29 |
| Total answer words | 2,710 |
| Words per puzzle | 22–72 (mean 46.7) |
| Word length | 4–13 characters |
| Users sampled per puzzle | 10,000 |
## Task
The NYT Spelling Bee presents 7 letters arranged in a honeycomb. Players must generate valid English words (4+ letters) using only those letters, with one designated center letter appearing in every word. Letters may be reused.
## Schema
| Column | Type | Description |
|--------|------|-------------|
| `date` | string | Puzzle date (YYYY-MM-DD) |
| `center_letter` | string | Required center letter |
| `outer_letters` | list[str] | 6 outer letters |
| `answer_words` | list[str] | All valid answer words |
| `answer_user_counts` | list[int] | Users (of 10,000) who found each word |
## Usage
```python
from datasets import load_dataset
ds = load_dataset("redasers/spelling-bee-human-difficulty")
puzzle = ds["train"][0]
# Print puzzle setup
print(f"Date: {puzzle['date']}")
print(f"Letters: {puzzle['outer_letters']} (center: {puzzle['center_letter']})")
# Find the hardest and easiest words
words = puzzle["answer_words"]
counts = puzzle["answer_user_counts"]
pairs = sorted(zip(words, counts), key=lambda x: x[1])
print(f"\nHardest: {pairs[0][0]} ({pairs[0][1] / 10_000:.1%} solve rate)")
print(f"Easiest: {pairs[-1][0]} ({pairs[-1][1] / 10_000:.1%} solve rate)")
```
## Data Source
Human solve frequencies were collected from the NYT Spelling Bee's public statistics, which report how many users found each answer word. A random sample of 10,000 users per puzzle provides the frequency counts.
## Associated Paper
Bryan E. Tuck and Rakesh M. Verma. [Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models](https://arxiv.org/abs/2511.21086). Accepted at LREC 2026.
## License
The puzzle structure and answer data are derived from the New York Times Spelling Bee game. This dataset is shared for non-commercial research purposes under fair use. The NYT retains all rights to the original game content.
## Citation
```bibtex
@misc{tuck2025orthographicconstraintsatisfactionhuman,
title={Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models},
author={Bryan E. Tuck and Rakesh M. Verma},
year={2025},
eprint={2511.21086},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2511.21086},
}
```
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