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README.md

@@ -9,6 +9,7 @@ tags:
 - human-difficulty
 - orthographic-constraints
 - llm-evaluation
+- benchmark
 size_categories:
 - n<1K
 task_categories:
@@ -35,9 +36,18 @@ dataset_info:
 
 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 Spelling Bee requires generating valid English words (4+ letters) using only 7 given letters. One designated center letter must appear in every word. Letters may be reused.
+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
 
@@ -57,14 +67,26 @@ from datasets import load_dataset
 ds = load_dataset("redasers/spelling-bee-human-difficulty")
 puzzle = ds["train"][0]
 
-# Human solve rate per word
-for word, count in zip(puzzle["answer_words"], puzzle["answer_user_counts"]):
-    print(f"{word}: {count / 10_000:.1%}")
+# 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. The center letter is uniquely determined as the only letter appearing in all answer words.
+
 ## Associated Paper
 
-> Bryan E. Tuck and Rakesh M. Verma. "Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models." arXiv:2511.21086, 2025.
+Bryan E. Tuck and Rakesh M. Verma. [Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models](https://arxiv.org/abs/2511.21086). arXiv:2511.21086, 2025.
 
 ## License