Retrieval Model’s Reasoning Skill Tested

The capacity of retrieval models to tackle reasoning problems within the RAG framework is explored. A discovered gap in behavior between these models and LLMs warrants a significant research shift.

• Questioning the ability of retrievers to solve reasoning problems.
• Instruction-aware IR models show deficiencies in inference time reasoning without instructions.
• Decoder-based embedding models promise improvements in retriever-LLM collaboration.
• Refinement via reranking models demonstrates substantial prospects in reasoning.

By highlighting the limitations and potential of retrieval models, this investigation calls for a strategic reorientation towards models proficient in reasoning. It implies a future where AI can more effectively understand and respond to complex inquiries. Read more