One potential limitation of the RaR approach is the reliance on the quality of the rephrased questions generated by the LLM. If the rephrased questions do not accurately capture the essence of the original question or introduce new ambiguities, it can lead to incorrect responses. To address this limitation, incorporating a feedback loop where human evaluators review and provide feedback on the rephrased questions can help improve their quality over time.
Another limitation is the scalability of the RaR method to handle a large volume of questions across diverse domains. To address this, automated techniques such as leveraging pre-trained models for question rephrasing or implementing a self-improvement mechanism where the LLM learns from its own rephrasing mistakes can enhance the scalability and efficiency of the RaR approach.
Furthermore, the RaR method may face challenges in handling highly specialized or domain-specific questions that require domain knowledge beyond the LLM's training data. To overcome this limitation, integrating domain-specific knowledge bases or expert systems to assist the LLM in rephrasing and responding to such questions can enhance its performance in specialized domains.