Enhancing Sequence Generation with Rollout Algorithms

Background

Generative pre-trained transformers (GPT) have grown in recent years in a number of natural language processing applications, largely due to the powerful openly available software and easy-to-use natural language interfaces. Transformers can provide a flexible and powerful model that generalizes the earlier forms of neural networks by using the attention mechanism and more complex nonlinearities. However, current GPT models often struggle with common sense reasoning, resulting in generated text that lacks logical coherence for word sequences.

Invention Description

Researchers at Arizona State University have developed a new rollout approach that can significantly improve the generation of word sequences in computational models. This approach leverages techniques including n-grams, transformers, Hidden Markov Models (HMMs), and Markov chains to strike a balance between greedy heuristic methods and computationally intensive sequence selection processes. This method is rooted in approximate dynamic programming, and can enhance sequence generation by considering future states for a more informed and balanced decision-making process.

Potential Applications:

• Advanced language processing tools and applications
• Enhanced predictive text and auto-complete features
• Improved sequence prediction in bioinformatics
• Optimized algorithms for content generation platforms

Benefits and Advantages:

• Improves accuracy – greater sequence generation accuracy over greedy heuristics
• Lowers costs – manages computational costs effectively
• Wide applicability – can be used in a wide range of models, including transformers and Markov chains
• Enhanced performance – uses variants including multi-step lookahead and truncated rollout

Related Publication: Most Likely Sequence Generation for n-Grams, Transformers, HMMs, and Markov Chains, by Using Rollout Algorithms