In addition to data contamination, several other factors may contribute to overfitting on established benchmarks for large language models:
Model Architecture: The design and complexity of the model architecture can influence its propensity to overfit. Models with excessive capacity or complexity may memorize training data instead of learning generalizable patterns.
Training Data Quality: The quality and representativeness of the training data play a crucial role in model performance. Biased or incomplete training data can lead to suboptimal generalization and increased overfitting.
Hyperparameter Tuning: Improper hyperparameter settings, such as learning rate, batch size, or regularization techniques, can impact the model's ability to generalize. Suboptimal hyperparameters may result in overfitting on specific benchmarks.
Fine-Tuning Strategies: The approach used for fine-tuning the model on benchmark datasets can also contribute to overfitting. Inadequate regularization during fine-tuning or excessive tuning on benchmark-specific data can lead to overfitting.
Evaluation Metrics: The choice of evaluation metrics and criteria can influence model performance. Metrics that do not fully capture the model's reasoning abilities or that incentivize memorization over genuine understanding can contribute to overfitting.
Model Selection Bias: The selection of models for evaluation and comparison can introduce bias. Models that are optimized specifically for benchmark performance may exhibit overfitting tendencies on those benchmarks.
Considering these factors alongside data contamination can provide a more comprehensive understanding of the challenges associated with overfitting in large language models on established benchmarks.