Fusion in Decoder

Fusion-in-Decoder (FiD) architectures are a key focus in improving the efficiency and effectiveness of retrieval-augmented language models, particularly for tasks involving long inputs like question answering and open-vocabulary object detection. Current research emphasizes optimizing FiD models for tasks such as prompt compression, improving the identification of causal relationships within retrieved information, and enhancing efficiency through techniques like hybrid pre-computed and on-the-fly encoding. These advancements aim to reduce computational costs while maintaining or improving accuracy, leading to more efficient and powerful natural language processing systems with broader applicability.