Update README.md

README.md

@@ -39,35 +39,92 @@ language:
 - en
 tags:
 - math
+- llm
+- benchmarks
+- saturation
+- evaluation
+- leaderboard
+- parameter-scaling
 ---
 
-# LLM Leaderboard Data for Hendrycks MATH Dataset
+# LLM Leaderboard Data for Hendrycks MATH Dataset (2022–2024)
 
-A Papers with Code attempt to aggregate yearly (2022-2024) LLM/Foundation model performance on Hendrycks' MATH evaluation.
+This dataset aggregates yearly performance (2022–2024) of large language models (LLMs) on the Hendrycks MATH benchmark. It is specifically compiled to explore performance evolution, benchmark saturation, parameter scaling trends, and evaluation metrics of foundation models solving complex math word problems.
 
-Data converted from source: [Math Word Problem Solving on MATH](https://paperswithcode.com/sota/math-word-problem-solving-on-math)
+Original source data: [Math Word Problem Solving on MATH (Papers with Code)](https://paperswithcode.com/sota/math-word-problem-solving-on-math)
 
-## Evaluation
+## About Hendrycks' MATH Benchmark
 
-Introduced by Hendrycks et al. in *Measuring Mathematical Problem Solving With the MATH Dataset*. MATH is a dataset comprising 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution useful for teaching models to generate answer derivations and explanations.
+Introduced by Hendrycks et al., the [MATH dataset](https://arxiv.org/abs/2103.03874) includes 12,500 challenging competition math problems, each accompanied by detailed solutions. These problems provide an ideal setting for evaluating and training AI models in advanced mathematical reasoning.
 
-## Visualizations
+## Dataset Highlights
 
-### Model Accuracy Trends
+- **Performance Evolution**: Significant increase in accuracy over three years (benchmark saturation analysis).
+- **Parameter Scaling**: Insight into how model size (parameters) correlates with accuracy improvements.
+- **Benchmark Saturation**: Clear evidence of performance brackets becoming saturated, indicating the need for new and more challenging mathematical reasoning benchmarks.
 
-![Model Accuracy Trends](img/model_accuracy_trends.png)
+## Key Insights from the Dataset (2022–2024)
 
-*Figure 1: Trends in model accuracy from 2022 to 2024, illustrating improvement rates over time.*
+- **Rapid Accuracy Gains**: Top model accuracy jumped dramatically—from approximately 65% in 2022 to nearly 90% in 2024.
+- **Performance Bracket Saturation**: Models achieving over 80% accuracy increased significantly, illustrating benchmark saturation and highlighting a potential ceiling in current dataset challenges.
+- **Efficiency in Parameter Scaling**: Smaller parameter models now perform tasks previously requiring large parameter counts, emphasizing efficiency gains alongside increased accuracy.
 
-### Top 20% Model Accuracy
+## Dataset Structure
 
-![Top 20% Model Accuracy](img/top_20_percent_accuracy.png)
+- **Number of Examples**: 112
+- **Data Format**: CSV (converted from Papers with Code)
+- **Features include**:
+  - Model ranking and year-specific accuracy
+  - Parameter counts and extra training data
+  - Direct links to relevant academic papers and model code
+
+## Practical Usage
 
-*Figure 2: Accuracy distribution among the top 20% performing models on the Hendrycks MATH dataset.*
+Here's how to quickly load and interact with the dataset:
 
-### Standard Deviation vs Median (Top 20%)
+```python
+from datasets import load_dataset
 
-![Top 20% Standard Deviation vs Median](img/top_20_std_vs_median.png)
+data = load_dataset("your_dataset_name_here")
+df = data['train'].to_pandas()
+df.head()
+```
 
-*Figure 3: Relationship between standard deviation and median accuracy scores for the top 20% models.*
+## Visualizations
+
+### Model Accuracy Improvement (2022–2024)
+![Model Accuracy Trends](img/model_accuracy_trends.png)
+*Rapid growth in top accuracy indicating approaching benchmark saturation.*
 
+### Accuracy Distribution Among Top 20%
+![Top 20% Model Accuracy](img/top_20_percent_accuracy.png)
+*Sharp increase in the number of high-performing models over three years.*
+
+### Parameter Scaling and Model Accuracy
+![Standard Deviation vs Median Accuracy](img/top_20_std_vs_median.png)
+*Visualizing consistency in accuracy improvements and the diminishing returns from scaling model parameters.*
+
+## Citation
+
+Please cite the original Hendrycks MATH dataset paper and this dataset aggregation/analysis:
+
+**MATH Dataset:**
+
+```bibtex
+@article{hendrycks2021math,
+  title={Measuring Mathematical Problem Solving With the MATH Dataset},
+  author={Hendrycks, Dan and Burns, Collin and Basart, Steven and Zou, Andy and Mazeika, Mantas and Song, Dawn and Steinhardt, Jacob},
+  journal={arXiv preprint arXiv:2103.03874},
+  year={2021}
+}
+```
+
+```bibtex
+@misc{nlile2024mathbenchmark,
+  author = {nlile},
+  title = {LLM Leaderboard Data for Hendrycks MATH Dataset (2022-2024): Benchmark Saturation and Performance Trends},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/nlile/math_benchmark_test_saturation/}
+}
+```