Generalizing Numerical Reasoning in table data through utilization of Primitive Descriptions and self-supervised learning
Authors: Hanjun Cho, Hanseong Kim, Jay-Yoon Lee


Reviewer paper summaries: This work addresses the challenges faced by language models in numerical reasoning tasks, particularly in the domain of finance. Specifically, the challenge is the models' limited generalization capability due to dependency on table headers and domain-specific jargon.

The authors propose a framework. It consists of  (1) incorporating primitive descriptions into queries; (2) header anonymization; (3) self-supervised learning.
Summary of strengths: 1. Anonymizing headers is a reasonable way to avoid spurious correlation.
Summary of weaknesses: 1. A few baselines might be missing:
- APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
- ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler

2. The proposed method is not clearly described. 
- Technical details of automatic data generation is important and should be described in the main text. What models or rules do you use to generate questions? 
- The primitive descriptions are a good intermediate explanations for connecting queries and answers. However, the process of obtaining descriptions during inference is not clearly described. 

I would revisit the value of the proposed framework if the authors could help me understand it.
Comments, suggestions and typos: See above.
Overall assessment scores: 2