Generalizability of NLP Models: A Multi-Dimensional Study
Authors: Ritam Dutt, Sagnik Ray Choudhury, Varun Venkat Rao, Carolyn Rose, V.G.Vinod Vydiswaran


Reviewer paper summaries: This paper investigates the generalizability of NLP models across multiple dimensions: domain shift, robustness, and compositional variations. The study evaluates various model architectures, sizes, and training strategies on their ability to generalize beyond their training datasets. The research highlights that model instances typically exhibit consistent generalization capabilities across different scenarios. The findings suggest that generalization performance is influenced by factors such as model architecture, base dataset performance, model size, and training mechanism.
Summary of strengths: This paper investigates different setting for training strategy, modeling, training datasets, and archiectures. The study's results can guide future model development and training practices to enhance generalization capabilities.
Summary of weaknesses: - Limited Base Datasets and base tasks: The conclusions are drawn from models trained on two base datasets and tasks (SNLI for NLI and SQuAD for MRC). Expanding the base datasets and tasks could provide a more comprehensive understanding of generalizability. 
- Analysis: The paper focuses primarily on empirical findings without delving deeply into the theoretical reasons behind the observed generalization patterns.
- The analysis only has generic datasets to specific datasets while missing the analysis on specific datasets to generic datasets (e.g., MNLI->SNLI).
- The target of this study is quite limited and outdated. The architectures being investigated are mostly not generative models and the conclusions are quite well-known....
Comments, suggestions and typos: No
Overall assessment scores: 2.5