1. Very limited reproducibility - Unless the authors release their training code, dialogue dataset, as well as model checkpoints, I find it very challenging to reproduce any of the claims in this paper. I encourage the authors to attach their code and datasets via anonymous repositories in the paper submission so that reviewers may verify the claims and try out the model for themselves.
2. Very high model complexity - The proposed paper employs a mathematically and computationally complex approach as compared to the textual input method. Does the proposed method outperform sending textual inputs to a large foundation model such as LLAMA or ChatGPT? The training complexity seems too high for any practical deployment of this model.
3. Dependent on the training data - I'm unsure if 44k dialogues is sufficient to capture a wide range of user traits and personalities across different content topics. LLMs are typically trained on trillions of tokens, I do not see how 44k dialogues can capture the combinations of personalities and topics. In theory, this dataset also needs to be massive to cover varied domains.
4. The paper is hard to read and often unintuitive. The mathematical complexity must be simplified and replaced with more intuitive design and modeling choice explanations so that readers may grasp core ideas faster.
3. Dependent on the training data - I'm unsure if 44k dialogues is sufficient to capture a wide range of user traits and personalities across different content topics. LLMs are typically trained on trillions of tokens, I do not see how 44k dialogues can capture the combinations of personalities and topics. In theory, this dataset also needs to be massive to cover varied domains.