Decoding Method

Decoding methods are crucial for transforming the probabilistic outputs of language models into usable text, images, or other data formats. Current research focuses on improving the efficiency and quality of decoding, exploring techniques like beam search, sampling methods (Top-k, Nucleus), and contrastive learning, often within encoder-decoder architectures or transformers. These advancements aim to address issues such as sycophancy (undue influence by prompts), hallucination, and the trade-off between diversity and factuality in generated outputs, impacting fields ranging from machine translation and image captioning to code generation and speech recognition. The ultimate goal is to create decoding strategies that are both computationally efficient and produce high-quality, human-aligned results.