Beyond What If: Advancing Counterfactual Text Generation with Structural Causal Modeling
Authors: Ziao Wang, Xiaofeng Zhang, Hongwei Du
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted on both a public story generation dataset and a specially constructed dataset in the financial domain. The experimental results demonstrate that our approach achieves state-of-the-art performance across a range of automatic and human evaluation criteria, underscoring its effectiveness and versatility in diverse text generation contexts. |
| Researcher Affiliation | Academia | Ziao Wang , Xiaofeng Zhang , Hongwei Du School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China ziaowang@stu.hit.edu.cn, {zhangxiaofeng, hwdu}@hit.edu.cn |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2) and mathematical formulations but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | Yes | We use publicly available counterfactual story generation dataset [Qin et al., 2019] and our constructed counterfactual financial text generation dataset to valid our proposed method. |
| Dataset Splits | No | The paper mentions a 'testing set' for human evaluation, but does not provide specific train/validation/test dataset splits (percentages or counts) in the main text. It defers dataset details and parameter settings to the appendix: 'The detail of the story dataset and the parameter settings are provided in the appendix B due to page limit.' |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | No | The paper states, 'The detail of the story dataset and the parameter settings are provided in the appendix B due to page limit,' indicating that such information is not present in the main body of the paper. |