Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study

@article{jiaxiang2026,
  title = {Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study},
  author = {Liu, Jiaxiang and Yuan, Chenhao and Xu, Shuwen and Xing, Boxuan and Huang, Xiusheng and Xu, Yinhao and Liu, Hao and Teng, Wenhao and Liao, Xiangwen and Cao, Pengfei and Zhao, Jun and Liu, Kang},
  journal = {Findings of the Association for Computational Linguistics: EMNLP 2026},
  year = {2026},
  month = {Jan},
  doi = {10.18653/2026.findings-emnlp.179},
  url = {https://doi.org/10.18653/2026.findings-emnlp.179},
}

Liu, Jiaxiang et al. (2026). Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study. Findings of the Association for Computational Linguistics: EMNLP 2026. https://doi.org/10.18653/2026.findings-emnlp.179

Liu, Jiaxiang, et al.. "Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study." Findings of the Association for Computational Linguistics: EMNLP 2026, 2026, https://doi.org/10.18653/2026.findings-emnlp.179.

Liu, Jiaxiang, et al.. "Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study." Findings of the Association for Computational Linguistics: EMNLP 2026 (2026). https://doi.org/10.18653/2026.findings-emnlp.179

Liu, Jiaxiang et al., 2026. Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study. Findings of the Association for Computational Linguistics: EMNLP 2026. Available at: <https://doi.org/10.18653/2026.findings-emnlp.179>.

TY  - JOUR
TI  - Quantifying Error Tolerance in Synthetic Data: An Atomic-level Operand vs. Operator Perturbation Study
AU  - Liu, Jiaxiang
AU  - Yuan, Chenhao
AU  - Xu, Shuwen
AU  - Xing, Boxuan
AU  - Huang, Xiusheng
AU  - Xu, Yinhao
AU  - Liu, Hao
AU  - Teng, Wenhao
AU  - Liao, Xiangwen
AU  - Cao, Pengfei
AU  - Zhao, Jun
AU  - Liu, Kang
PY  - 2026
JO  - Findings of the Association for Computational Linguistics: EMNLP 2026
DO  - 10.18653/2026.findings-emnlp.179
UR  - https://doi.org/10.18653/2026.findings-emnlp.179
ER  -