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@@ -14,29 +14,6 @@ tags:
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  size_categories:
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  - n<1K
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  pretty_name: LyTOC Benchmark
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- configs:
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- - config_name: default
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- data_files:
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- - split: train
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- path: data/train-*
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- dataset_info:
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- features:
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- - name: homework
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- dtype: string
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- - name: exercise_number
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- dtype: string
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- - name: sub_problem
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- dtype: string
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- - name: content
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- dtype: string
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- - name: full_id
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- dtype: string
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- splits:
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- - name: train
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- num_bytes: 6972
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- num_examples: 28
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- download_size: 6910
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- dataset_size: 6972
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  ---
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  # LyTOC Benchmark Dataset
@@ -47,13 +24,13 @@ A curated collection of Theory of Computation and Algorithms homework exercises,
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  ### Dataset Summary
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- The LyTOC (Logic and Theory of Computation) Benchmark contains 28 carefully extracted exercises from 9 homework assignments covering fundamental topics in theoretical computer science. Each exercise is preserved with its original LaTeX mathematical notation, making it suitable for evaluating language models on formal reasoning tasks.
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  **Key Features:**
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- - 28 exercises across 9 homework assignments
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  - Topics: automata theory, complexity theory, Turing machines, formal languages, algorithm analysis
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  - LaTeX mathematical notation preserved
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- - Structured with exercise numbers and sub-problems
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  - Clean extraction with OCR post-processing
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  ### Supported Tasks
@@ -71,13 +48,12 @@ The LyTOC (Logic and Theory of Computation) Benchmark contains 28 carefully extr
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  ### Data Instances
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- Each instance represents a single exercise or sub-problem:
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  ```json
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  {
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  "homework": "hw1",
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  "exercise_number": "3",
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- "sub_problem": null,
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  "content": "Let $\\Sigma = \\{0, 1\\}$. Let language\n\n$$L = \\{w \\in \\{0, 1\\}^* : w \\text{ has an unequal number of 0's and 1's}\\}.$$\n\nProve $L^* = \\Sigma^*$.",
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  "full_id": "hw1_ex3"
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  }
@@ -87,19 +63,17 @@ Each instance represents a single exercise or sub-problem:
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  - `homework` (string): Homework identifier (e.g., "hw1", "hw2", "hw13")
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  - `exercise_number` (string): Exercise number within the homework (e.g., "1", "2", "3")
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- - `sub_problem` (string or null): Sub-problem identifier if the exercise has multiple parts (e.g., "1", "2")
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  - `content` (string): Full exercise text including LaTeX mathematical notation
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  - `full_id` (string): Unique identifier for the exercise (e.g., "hw1_ex3", "hw2_ex3_1")
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  ### Data Splits
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- The dataset consists of a single split containing all 28 exercises.
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  ## Dataset Statistics
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- - **Total Exercises**: 28
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  - **Homeworks**: 9 (hw1, hw2, hw3, hw5, hw6, hw9, hw10, hw11, hw13)
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- - **Exercises with Sub-problems**: 2
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  - **Average Content Length**: ~200-500 characters per exercise
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  ### Topic Distribution
@@ -132,7 +106,6 @@ The dataset was created from homework assignments in a Theory of Computation and
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  2. **OCR Processing**: Extract text using SimpleTex OCR API
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  3. **Punctuation Normalization**: Convert Chinese punctuation to English equivalents
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  4. **Exercise Splitting**: Use regex patterns to identify exercise boundaries
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- 5. **Sub-problem Detection**: Identify and separate sub-problems within exercises
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  6. **Metadata Generation**: Create unique identifiers and structure data
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  ### Annotations
@@ -199,7 +172,7 @@ For questions or issues regarding this dataset, please open an issue on the data
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  from datasets import load_dataset
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  # Load the dataset
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- dataset = load_dataset("lytoc-benchmark")
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  # Access an exercise
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  exercise = dataset['train'][0]
@@ -213,4 +186,4 @@ print(f"Homework 1 has {len(hw1_exercises)} exercises")
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  ## Version History
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- - **v1.0.0** (2025-12-30): Initial release with 28 exercises from 9 homework assignments
 
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  size_categories:
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  - n<1K
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  pretty_name: LyTOC Benchmark
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # LyTOC Benchmark Dataset
 
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  ### Dataset Summary
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+ The LyTOC (Logic and Theory of Computation) Benchmark contains 27 carefully extracted exercises from 9 homework assignments covering fundamental topics in theoretical computer science. Each exercise is preserved with its original LaTeX mathematical notation, making it suitable for evaluating language models on formal reasoning tasks.
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  **Key Features:**
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+ - 27 exercises across 9 homework assignments
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  - Topics: automata theory, complexity theory, Turing machines, formal languages, algorithm analysis
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  - LaTeX mathematical notation preserved
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+ - Structured with exercise numbers
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  - Clean extraction with OCR post-processing
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  ### Supported Tasks
 
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  ### Data Instances
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+ Each instance represents a single exercise:
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  ```json
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  {
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  "homework": "hw1",
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  "exercise_number": "3",
 
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  "content": "Let $\\Sigma = \\{0, 1\\}$. Let language\n\n$$L = \\{w \\in \\{0, 1\\}^* : w \\text{ has an unequal number of 0's and 1's}\\}.$$\n\nProve $L^* = \\Sigma^*$.",
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  "full_id": "hw1_ex3"
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  }
 
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  - `homework` (string): Homework identifier (e.g., "hw1", "hw2", "hw13")
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  - `exercise_number` (string): Exercise number within the homework (e.g., "1", "2", "3")
 
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  - `content` (string): Full exercise text including LaTeX mathematical notation
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  - `full_id` (string): Unique identifier for the exercise (e.g., "hw1_ex3", "hw2_ex3_1")
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  ### Data Splits
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+ The dataset consists of a single split containing all 27 exercises.
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  ## Dataset Statistics
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+ - **Total Exercises**: 27
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  - **Homeworks**: 9 (hw1, hw2, hw3, hw5, hw6, hw9, hw10, hw11, hw13)
 
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  - **Average Content Length**: ~200-500 characters per exercise
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  ### Topic Distribution
 
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  2. **OCR Processing**: Extract text using SimpleTex OCR API
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  3. **Punctuation Normalization**: Convert Chinese punctuation to English equivalents
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  4. **Exercise Splitting**: Use regex patterns to identify exercise boundaries
 
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  6. **Metadata Generation**: Create unique identifiers and structure data
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  ### Annotations
 
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  from datasets import load_dataset
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  # Load the dataset
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+ dataset = load_dataset("Zecyel/LyTOC")
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  # Access an exercise
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  exercise = dataset['train'][0]
 
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  ## Version History
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+ - **v1.0.0** (2025-12-30): Initial release with 27 exercises from 9 homework assignments