Extractive Rationalization and Model Accuracy in Few-Shot Learning
Authors: Karl Elbakian, Samuel Carton


Reviewer paper summaries: In this work, the authors investigate the relationship between prediction and extractive rationalization in a few-shot learning setting. They find a strong correlation between classification accuracy and consistency with human-annotated rationales. They also discover that model errors are more attributable to inaccurate rationalization rather than incomplete rationalization.
Summary of strengths: - The experimental setup is described clearly, utilizing the latest LLM, GPT-4, for the experiments.
- The experimental results are illustrated with examples.
Summary of weaknesses: - The paper lacks a clear explanation of the motivation for this work.
- The experimental methods are very simple and lack technical depth, making it resemble a course assignment report rather than a cutting-edge scientific research paper. Additionally, no valuable insights are evident from the experimental results.
Comments, suggestions and typos: - It would be helpful to clearly explain the significance of the research problem.
- There is room for improvement in the writing of the paper.
Overall assessment scores: 1