Large Language Models in Real-World Table Task Workflows: A Survey
Authors: Jiaming Tian, Lingxin Wang, Liyao Li, Wentao Ye, Haobo Wang, Zujie Ren, Gang Chen, Junbo Zhao


Reviewer paper summaries: This is a literature survey on LLM applications in tabular data task pipelines.
Summary of strengths: * The paper is well-motivated---the world is awash in tabular data and related tasks.  

* It does a good job of providing a broad overview of the landscape.
Summary of weaknesses: * Since it prioritizes breadth, the paper ends up lacking depth.  The discussions around challenges and research directions are often brief and vague, turning the paper into a laundry list of prior work.  No coherent vision is provided.
Comments, suggestions and typos: * There are two directions from here: either go deeper while keeping the survey coverage the same, targeting a venue that would be receptive to the extended paper length, or narrow the scope and go deeper and provide a vision. It is OK to be opinionated. 

* Contrast the contributions of your survey with that of Wang et al. 2024a. Both papers look similar. Adding a brief related work section discussing similar surveys may be helpful.
Overall assessment scores: 1.5