While utilizing a single LLM backbone for both semantic tokenization and generative recommendation offers several advantages, such as reduced complexity and improved knowledge transfer, there are potential limitations and drawbacks:
Resource Constraints: A single LLM backbone may face resource limitations, particularly in terms of memory and computational power. As the model needs to handle both tokenization and recommendation tasks, it may require significant resources, which could hinder scalability, especially in real-time applications.
Overfitting Risks: Relying on a single model for multiple tasks can increase the risk of overfitting, particularly if the model is not sufficiently regularized. This could lead to suboptimal performance in either task, especially if the training data for one task is not representative of the other.
Task Interference: The dual role of the LLM may lead to task interference, where the optimization for one task negatively impacts the performance of the other. For instance, the model might prioritize generating recommendations over accurately tokenizing semantic representations, or vice versa.
Limited Specialization: Different tasks may benefit from specialized architectures or training regimes. A single LLM may not be able to fully exploit the unique characteristics of each task, potentially leading to less effective performance compared to using dedicated models.
Complexity in Fine-Tuning: Fine-tuning a single LLM for both tasks can be complex, as the model must balance the learning objectives of both semantic tokenization and generative recommendation. This could complicate the training process and require careful tuning of hyperparameters to achieve optimal performance.
Domain Adaptation Challenges: If the LLM is pretrained on a general corpus, it may struggle to adapt to domain-specific nuances in the recommendation context. This could limit its effectiveness in generating high-quality recommendations based on specialized item content.
Addressing these limitations may require careful design considerations, such as implementing modular components within the framework or exploring hybrid approaches that combine the strengths of multiple models.