Text Generation

Text generation studies how models produce language for tasks such as dialogue, summarization, storytelling, translation, and structured reports. Autoregressive transformers generate token by token, while masked-diffusion and flow-based models explore parallel or few-step generation; decoding and activation steering offer ways to adjust outputs without retraining. For longer or constrained tasks, systems combine planning, retrieval, structured representations, editing, and feedback-based training such as reinforcement learning. A central challenge is balancing speed and instruction-following with factuality, diversity, cultural fit, and quality that human readers and domain experts value.