Contrastive Search for Text Generation

Introduced Contrastive Search, a novel decoding method for neural text generation that jointly optimizes for model confidence and a degeneration penalty. This method aims to produce human-level text by avoiding issues like repetition and semantic incoherence found in traditional greedy, beam, top-k, and nucleus sampling methods. It is integrated into the 🤗 Transformers library and demonstrated with examples and a live demo.