While the generative retrieval approach offers several advantages over contrastive retrieval, it is not without its limitations. One potential drawback is the increased computational complexity associated with generating sentences and modeling dependencies, which may lead to longer inference times compared to the more straightforward contrastive retrieval method. This can be particularly challenging in real-time applications where speed is critical.
Another limitation is the reliance on the quality of the pre-trained prefixLM model. If the model has not been adequately trained on diverse and representative datasets, it may struggle to generate accurate or contextually relevant sentences, leading to suboptimal performance in attribute recognition tasks. Additionally, the generative retrieval approach may be sensitive to the choice of sentence templates, and poorly designed templates could hinder the model's ability to capture the necessary dependencies.
To address these limitations, several strategies can be employed. First, optimizing the model architecture for efficiency, such as implementing pruning techniques or using lighter-weight transformer variants, can help reduce inference time. Second, enhancing the pre-training phase by incorporating more diverse and high-quality datasets can improve the model's robustness and generalization capabilities. Finally, conducting systematic evaluations of various sentence templates during the development phase can ensure that the most effective structures are utilized, thereby maximizing the performance of the generative retrieval approach.