reviews: 
Comments: This paper investigates the effectiveness of various data augmentation techniques for training localized, personalized AI models using LLaMA3, fine-tuned through LoRA. The study employs methods like random deletion, synonym replacement, backtranslation, and paraphrasing to enhance the quality and robustness of personalized dialogue models trained on character-specific data. Experiments are conducted on two datasets with distinct linguistic styles, aiming to provide a detailed comparison of augmentation techniques in low-resource, domain-specific training scenarios.

I find that while this paper tackles a relevant problem and provides an insightful comparison of augmentation techniques, its contributions remain limited. The paper needs a more novel methodological approach or a broader set of experiments that highlight its findings' relevance to a wider range of personalized AI training scenarios. Therefore, I unfortunately cannot recommend acceptance for this paper; however, I have included some suggestions for improvements in my comments below.
Strengths: 1. **Clear and Relevant Problem Statement**: The scarcity of personalized dialogue data is an issue of practical importance for character-based AI models. The study addresses this with a focus on data augmentation, an area less explored in NLP.
2. **Multiple Metrics Used**: The paper evaluates on both training and validation loss, as well as BLEU and ROUGE scores, providing valuable insights for optimizing dialogue data augmentation.
3. **Resource-Conscious Approach**: The authors' choice of LoRA for fine-tuning and Unsloth for training efficiency is thoughtful and appropriate for low-resource environments, demonstrating an understanding of practical constraints in the field.
Weaknesses: 1. **Incremental Contributions**: While the topic of data augmentation in NLP is valuable, the paper’s contributions appear largely incremental. Many of the techniques explored (e.g., synonym replacement, backtranslation) are well-known and widely used. The paper could benefit from exploring more novel augmentation techniques tailored to character-based dialogue data's nuances. Additionally, *about half of the paper is describing past techniques* like EDA, Paraphrasing, LoRA, etc (specifically, Section 2, and 3), and there is nearly not enough content for this paper to be considered an impactful contribution (figure 3 has too little information to be taking a whole page and Table 1 font seems overtly big as if just to fill some space). 
2. **Limited Dataset Scope**: Only two datasets are used, both of which are highly specific in context (e.g., Paimon from *Genshin Impact*). This narrow focus raises questions about the generalizability of the findings to other personalized dialogue systems or broader NLP applications. Including a wider variety of datasets or character types would add robustness to the conclusions.
3. **Paraphrasing Overfitting Concerns**: The paper notes overfitting issues with the paraphrasing technique but does not fully explore ways to mitigate this. Although the authors acknowledge the limitations of paraphrasing with large models in domain-specific contexts, a more thorough investigation into alternative paraphrasing techniques or domain adaptation methods would strengthen the study.
4. **Issues With the References**: a) There are some invalid entries in the references, such as, "Unknown Author" and b) The reference list is too short. I suggest the authors to conduct a more thorough review of the current literature.

### Suggestions

1. **Broaden Dataset Variety**: I think the authors are already aware of this problem, but including datasets that represent a wider range of conversational styles or character personalities would improve the generalizability of the results. Alternatively, conducting a detailed analysis of *why* certain augmentation techniques are particularly suited or unsuited to the specific characteristics of these datasets would be more interesting.
2. **Investigate Advanced Augmentation Techniques**: Consider exploring novel or advanced data augmentation techniques that could better align with the semantic and contextual requirements of character dialogue, such as sentiment-aware or context-sensitive paraphrasing. Additionally, using domain-adaptive techniques for paraphrasing may mitigate overfitting while still generating diverse examples. There is also a huge body of work on using off-the-shelf LLMs for data augmentation that the authors should explore.
3. **Refine Analysis of Computational Constraints**: Given the choice to work with the smaller LLaMA3-8B model, it would be beneficial to address the specific trade-offs involved more explicitly. This discussion could include any adjustments made to balance memory efficiency with model performance and how this may limit model generalization capabilities.
4. **Improve Paper Organization**: We don't need to describe past methods like EDA, LoRA, etc. in so much detail. Instead, I would suggest focusing on including more strong baselines (like LLM-based data augmentation).
Recommendation: 1
Reviewer confidence: 5
Soundness/correctness: 2
Clarity: 1