We need to talk about random seeds

Abstract: Modern neural network libraries all take as a hyperparameter a random seed, typically used to determine the initial state of the model parameters. This opinion piece argues that there are some safe uses for random seeds: as part of the hyperparameter search to select a good model, creating an ensemble of several models, or measuring the sensitivity of the training algorithm to the random seed hyperparameter. It argues that some uses for random seeds are risky: using a fixed random seed for "replicability" and varying only the random seed to create score distributions for performance comparison. An analysis of 85 recent publications from the ACL Anthology finds that more than 50% contain risky uses of random seeds.

• The paper demonstrates that treating random seeds as tunable hyperparameters can optimize model selection and enhance ensemble accuracy.
• It details methodologies for sensitivity analysis and ensemble creation to improve model stability amid inherent stochasticity.
• Analysis of 85 ACL studies reveals over 50% risky fixed-seed practices, underscoring the need for community-wide best practices.

Random Seeds in Neural Network Training

The utilization of random seeds in neural network training is a critical consideration that influences both the initialization of model parameters and the stochastic elements of training processes such as dropout and minibatch composition. This paper, "We need to talk about random seeds" (2210.13393), discusses the implications of these random seeds as hyperparameters in NLP models. It assesses both the safe and risky employment of random seeds and evaluates their impact across 85 NLP articles, identifying prevalent misconceptions and practices.

Random Seed Utilization: Safe Practices

Model Selection

Model selection benefits from treating random seeds as a tunable hyperparameter, akin to learning rates or regularization strengths. By optimizing the random seed, one ensures that model initialization contributes effectively to achieving optimal performance on validation sets. This approach compensates for the intrinsic stochasticity in neural networks by evaluating models across diverse random seeds and selecting configurations that demonstrate superior validation performance.

Ensemble Creation

Random seeds also facilitate the construction of ensemble models by training multiple instances of the same architecture under different seeds. This method leverages the variability introduced by random seeds to enhance model robustness and accuracy through ensemble voting mechanisms, effectively combining predictions from models with varying parameter initializations.

Sensitivity Analysis

Conducting sensitivity analysis concerning random seeds provides insight into a model's stability under variations of this hyperparameter. This practice helps in assessing the resilience of models and quantifying their sensitivity, ultimately guiding the design of more robust architectures by understanding how performance variability is affected by seed choices.

Risky Employment of Random Seeds

Single Fixed Seed

Employing a single fixed random seed intending to achieve replicability is risky and potentially misleading. This method assumes deterministic replication across various computational environments, which is often invalid due to non-deterministic factors, including hardware-specific computation discrepancies. It fails to capture the model's performance potential across varied initializations, leading to suboptimal hyperparameter configurations and performance metrics.

Performance Comparisons

Using random seed variability for generating performance distributions poses risks in performance comparison tasks. It results in generating distributions that only reflect variability due to seed differences rather than exploring hyperparameter space comprehensively. Comparisons based on such distributions might misguide conclusions about model superiority, as they overlook the broader variability induced by other hyperparameters.

Analysis of ACL Anthology Publications

The paper reviews 85 articles from the ACL Anthology, identifying that more than 50% incorporate risky practices in using random seeds. This statistic underscores the broader challenge within the NLP community regarding the understanding and implementation of safe practices with random seed utilization. The analysis calls attention to the consistent lack of distinction between safe and risky practices, necessitating improved guidance and educational efforts within NLP research and development.

Discussion on Community Practices

Transitioning away from risky applications of random seeds requires collective awareness and educational initiatives within the NLP research community. Enhanced mentoring, rigorous peer-review processes, and thorough understanding of random seed implications on neural network performance are essential for cultivating best practices. Additionally, adopting systematic hyperparameter optimization processes that treat random seeds equivalently aids in characterizing models more effectively.

Conclusion

This examination presents a coherent framework for using random seeds, highlighting safe methodologies and cautioning against widespread risky practices in NLP model development. The findings reveal a significant portion of current research employs potentially misleading approaches with random seeds. Emphasizing thorough hyperparameter optimization, including that of random seeds, will contribute to more robust and replicable models in neural network research. The recommendations aim to stimulate adoption of best practices, ensuring improvements in training protocols and performance evaluations of NLP systems.