Controllable Text Generation
Overview
Controllable text generation studies how to make generated language reliably match goals such as a requested style, sentiment, reading level, length, format, or safety condition. Approaches include prompts and control tokens, fine-tuning and preference optimization, decoding-time constraints and guidance, activation steering, and planning or feedback-based revision; some methods also control generation in diffusion models. These techniques support applications from education and creative writing to healthcare, where outputs may need to meet several requirements at once. A central challenge is balancing reliable constraint satisfaction with fluency, factuality, diversity, and user-specific preferences, while evaluating systems consistently across tasks and languages.
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