Adapting the SEE model to work with different pre-trained language models beyond BART involves several considerations. First, the architecture of the chosen language model must be compatible with the existing framework, particularly in terms of input-output dimensions and attention mechanisms. For instance, models like GPT or T5 could be integrated by modifying the input processing pipeline to accommodate their specific tokenization and embedding strategies.
The implications for performance and generalization would depend on the characteristics of the new language model. For example, using a model with a larger parameter size or more extensive training data could enhance the model's ability to generate coherent and contextually relevant text from EEG signals. However, this could also lead to increased computational requirements and longer training times.
Moreover, different language models may have varying strengths in handling specific language tasks, which could affect the SEE model's performance in EEG-to-Text translation. For instance, a model fine-tuned for conversational tasks might yield better results in dialogue generation scenarios. Therefore, careful evaluation and possibly fine-tuning of the new language model on relevant datasets would be essential to ensure optimal performance and generalization across diverse language tasks.