Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task

Author:

Dong Guanting,Zhao Jinxu,Hui Tingfeng,Guo Daichi,Wang Wenlong,Feng Boqi,Qiu Yueyan,Gongque Zhuoma,He Keqing,Wang Zechen,Xu Weiran

Publisher

Springer Nature Switzerland

Reference28 articles.

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2. Brown, T.B., et al.: Language models are few-shot learners (2020)

3. Coucke, A., et al.: Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces (2018)

4. Dong, G., et al.: PSSAT: a perturbed semantic structure awareness transferring method for perturbation-robust slot filling. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 5327–5334. International Committee on Computational Linguistics, Gyeongju (2022). https://aclanthology.org/2022.coling-1.473

5. Gopalakrishnan, K., Hedayatnia, B., Wang, L., Liu, Y., Hakkani-Tur, D.: Are neural open-domain dialog systems robust to speech recognition errors in the dialog history? An empirical study (2020)

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