Technical guide to controllable neural text generation approaches

This technical survey examines methods for steering unconditioned language models to generate text with specific attributes such as topic, style, and sentiment. The article reviews multiple approaches for controlled content generation, addressing a key challenge in deploying language models for applications requiring output consistency. Decoding strategies are explored as mechanisms to restrict or prefer certain outputs without modifying model weights. The guide covers temperature-based sampling control and other token-level steering techniques. Various control methods are presented with their respective advantages and disadvantages, recognizing that model steering remains an active research question with no universally optimal solution.