| ---
|
| license: other
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| license_name: fair-use-research
|
| language:
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| - en
|
| tags:
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| - spelling-bee
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| - word-games
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| - human-difficulty
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| - orthographic-constraints
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| - llm-evaluation
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| - benchmark
|
| size_categories:
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| - n<1K
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| task_categories:
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| - text-generation
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| pretty_name: NYT Spelling Bee Human Difficulty
|
| dataset_info:
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| features:
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| - name: date
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| dtype: string
|
| - name: center_letter
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| dtype: string
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| - name: outer_letters
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| sequence: string
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| - name: answer_words
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| sequence: string
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| - name: answer_user_counts
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| sequence: int32
|
| splits:
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| - 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) |
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| | Word length | 4–13 characters |
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| | 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 |
|
| |--------|------|-------------|
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| | `date` | string | Puzzle date (YYYY-MM-DD) |
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| | `center_letter` | string | Required center letter |
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| | `outer_letters` | list[str] | 6 outer letters |
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| | `answer_words` | list[str] | All valid answer words |
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| | `answer_user_counts` | list[int] | Users (of 10,000) who found each word |
|
|
|
| ## Usage
|
|
|
| ```python
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| from datasets import load_dataset
|
|
|
| ds = load_dataset("redasers/spelling-bee-human-difficulty")
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| puzzle = ds["train"][0]
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|
|
| # Print puzzle setup
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| print(f"Date: {puzzle['date']}")
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| print(f"Letters: {puzzle['outer_letters']} (center: {puzzle['center_letter']})")
|
|
|
| # Find the hardest and easiest words
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| words = puzzle["answer_words"]
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| counts = puzzle["answer_user_counts"]
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| pairs = sorted(zip(words, counts), key=lambda x: x[1])
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|
|
| print(f"\nHardest: {pairs[0][0]} ({pairs[0][1] / 10_000:.1%} solve rate)")
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| 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
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| @misc{tuck2025orthographicconstraintsatisfactionhuman,
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| title={Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models},
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| author={Bryan E. Tuck and Rakesh M. Verma},
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| year={2025},
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| eprint={2511.21086},
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| archivePrefix={arXiv},
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| primaryClass={cs.CL},
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| url={https://arxiv.org/abs/2511.21086},
|
| }
|
| ```
|
|
|
