Expertise-Centric Prompting Framework for Financial Tabular Data Generation using Pre-trained Large Language Models
Authors: Subin Kim, Jungmin Son, Minyoung Jung, Youngjun Kwak


Reviewer paper summaries: * This paper proposes an expertise-centric prompting (ECP) framework to synthesize financial data with the help of financial experts and large language models (LLMs).
* In addition, this paper proposes a new evaluation metric to test the realism and diversity of synthesized financial datasets.
* The experimental results demonstrate that the proposed method improves the diversity of the pseudo-financial data.
Summary of strengths: 1. The proposed framework and evaluation metrics are well-elaborated.
2. The experimental results show the effectiveness of the proposed method in generating diverse pseudo-financial data.
Summary of weaknesses: 1. The proposed method is complicated and involves domain experts in the loop, which can be costly and inefficient.
2. The newly proposed method is evaluated on the metric introduced in this paper, making the experimental results somewhat less convincing.
3. The code (ECP framework and the evaluation metric), prompts, and trained models are not promised to be released.
4. See "Comments, Suggestions And Typos".
Comments, suggestions and typos: **Comments**

* I respectfully doubt the significance of such pseudo-financial datasets, because however plausible the synthesized data samples are (with financial experts providing relevant expertise), they are not authentic and user-oriented. It can be quite different from the real financial scenarios.

**Suggestions**

* Avoid proposing a new method and a new evaluation metric at the same time.
Overall assessment scores: 3