Introducing PLANNER: A New Approach to AI Text Generation
Text generation is a crucial aspect of artificial intelligence (AI) technology. Autoregressive models, widely used for text generation, often produce repetitive and low-quality output due to the accumulation of errors during generation. This is known as exposure bias.
The Challenge of Text Generation in AI
Denoising diffusion models offer an alternative approach, allowing a model to revisit and revise its output. However, they can be computationally expensive and often produce less fluent output compared to autoregressive models, especially for longer text and paragraphs.
The Solution: PLANNER
In a recent paper, researchers proposed PLANNER – a model that combines latent semantic diffusion with autoregressive generation to produce fluent text while exerting global control over paragraphs. The model achieves this by integrating an autoregressive “decoding” module with a “planning” module that uses latent diffusion to generate semantic paragraph embeddings in a coarse-to-fine manner.
Effectiveness of PLANNER
Through various conditional generation tasks, PLANNER has been shown to be effective in producing high-quality, long-form text efficiently. Its performance in semantic generation, text completion, and summarization has demonstrated its potential to revolutionize AI text generation.