To mitigate the impact of suboptimal embedding model performance on the conformal prediction-based retrieval process, several techniques can be employed. Firstly, optimizing the embedding model parameters and architecture to enhance its ability to capture semantic similarities and nuances in the text data can improve performance. Fine-tuning the embedding model on domain-specific data or incorporating pre-trained embeddings tailored to the specific knowledge base can also boost performance.
Additionally, implementing ensemble methods by combining multiple embedding models or using ensemble learning techniques can help mitigate the impact of individual model weaknesses. Regular monitoring and evaluation of the embedding model's performance using validation metrics and feedback from the retrieval process can aid in identifying and addressing any shortcomings. Employing techniques like transfer learning to leverage knowledge from related tasks or domains can further enhance the embedding model's performance in the conformal prediction-based retrieval process.