A Multi-Task Embedder For Retrieval Augmented LLMs

Understand

LLMs confront inherent limitations in terms of its knowledge, memory, and action.

• The retrieval augmentation stands as a vital mechanism to address these limitations, which brings in useful information from external sources to augment the LLM.
• However, existing retrieval methods encounter two pressing issues.
• On one hand, the general retrievers are not properly optimized for retrieval augmentation hence exhibit limited effectiveness; on the other hand, the task-specific retrievers excel in the targeted retrieval augmentation scenario, while lack the versatility to handle diverse scenarios.