An LP-based hyperparameter optimization model for language modeling
This addresses hyperparameter tuning for language modeling, but it is incremental as it applies known optimization techniques to a specific cost function.
The authors tackled hyperparameter optimization for language models by proposing a fractional nonlinear programming model approximated as a linear programming model, which they applied to a real-world dataset and showed results in lower perplexity values compared to grid search.
In order to find hyperparameters for a machine learning model, algorithms such as grid search or random search are used over the space of possible values of the models hyperparameters. These search algorithms opt the solution that minimizes a specific cost function. In language models, perplexity is one of the most popular cost functions. In this study, we propose a fractional nonlinear programming model that finds the optimal perplexity value. The special structure of the model allows us to approximate it by a linear programming model that can be solved using the well-known simplex algorithm. To the best of our knowledge, this is the first attempt to use optimization techniques to find perplexity values in the language modeling literature. We apply our model to find hyperparameters of a language model and compare it to the grid search algorithm. Furthermore, we illustrating that it results in lower perplexity values. We perform this experiment on a real-world dataset from SwiftKey to validate our proposed approach.