Output Embeddings

Output embeddings, vector representations of model outputs, are central to improving various natural language processing (NLP) tasks. Current research focuses on refining these embeddings through contrastive learning, leveraging large language models (LLMs) for improved context modeling, and employing techniques like targeted concept removal to address bias and enhance fairness. These advancements are impacting downstream applications, such as improved automatic speech recognition, more effective task-oriented dialog systems, and enhanced knowledge graph embedding, ultimately leading to more robust and reliable NLP systems.