Simple tree-search techniques for LLM token sampling

Nov 27, 2023 ·

Sampling strategies are important tools in language models (LMs). In particular, sampling from an LM can help to reduce the complexity of input data, while maintaining the accuracy of predictions. This article explores several types of sampling strategies used for LMs and provides an overview of their advantages and limitations.

The first sampling strategy is Viterbi sampling, which samples sequences based on their probability scores. This method is useful when dealing with large datasets and can help to reduce computational expense. However, it does not always produce accurate results, and can be prone to overfitting.

Another sampling strategy is importance sampling, which weights data points according to their importance to the model. This approach can be helpful when dealing with complex datasets, as it allows the model to focus on the most significant elements. However, it can lead to bias if certain data points are given too much weight.

A third strategy is Thompson sampling, which randomly selects items from a probability distribution. This approach is commonly used in reinforcement learning, as it helps to reduce the search space and encourages exploration of new possibilities. However, it can be computationally expensive, and there is no guarantee that the best solutions will be chosen.

Finally, Gibbs sampling is a method used for approximate Bayesian inference. This technique uses Markov Chain Monte Carlo methods to sample from a probability distribution, allowing for efficient exploration of the parameter space. However, it can be difficult to converge to the true maximum likelihood estimates.

Overall, each of these sampling strategies has its own benefits and drawbacks. As such, it is important to consider the context before selecting the most appropriate sampling strategy. By doing so, you can ensure that your language model is able to accurately predict the outcomes of your data points, while minimizing computational costs.