RACC's strategies for compressing and aggregating retrieved contexts can be effectively extended to various retrieval-augmented generation (RAG) tasks beyond knowledge-based visual question answering (KB-VQA) by leveraging its core principles of context compression, aggregation, and efficient modulation. For instance, in tasks such as text summarization or dialogue generation, the process of compressing lengthy documents into concise prompts can enhance the model's ability to focus on relevant information while minimizing input token count.
The use of a hyperMLLM for context compression can be adapted to summarize large text corpora into manageable prompts that retain essential information, thereby improving the efficiency of downstream models. Additionally, the aggregation strategies employed in RACC, such as Decoupled Compression of Vision and Question (DCVQ) and Retrieval-Guided Cross-Attention (RGCA), can be modified to enhance the relevance of retrieved contexts in other domains. For example, in a document retrieval task, these strategies could prioritize the most relevant sections of a document based on user queries, ensuring that the generated output is contextually rich and pertinent.
Moreover, the Pseudo-Relevance-based Backpropagation Dropout (PRDB) strategy can be utilized to filter out irrelevant information during training, which is crucial in tasks where noise in the data can lead to poor model performance. By applying these strategies across different RAG tasks, models can achieve improved accuracy and efficiency, making them more suitable for real-world applications where context and relevance are paramount.