Limitations and future directions
While promising, RAG models also come with challenges and open research problems, including the following:
- Knowledge selection:One of the critical challenges in RAG is determining the most relevant and salient knowledge to retrieve from the knowledge base. With vast amounts of information available, it becomes crucial to identify and prioritize the most pertinent knowledge for the given context. Existing retrieval methods may struggle to capture the nuances and subtleties of the query, leading to the retrieval of irrelevant or tangential information. Developing more sophisticated query understanding and knowledge selection mechanisms is a key area of research.
- Knowledge grounding: Seamlessly integrating retrieved knowledge into the generation process is a non-trivial task. RAG models need to understand the retrieved knowledge, reason over it, and coherently weave it into the generated text. This process requires advanced NL understanding...
