Challenges and Opportunities in Text Generation Explainability

Author:

Amara KenzaORCID,Sevastjanova RitaORCID,El-Assady Mennatallah

Publisher

Springer Nature Switzerland

Reference56 articles.

1. Ács, J., Kádár, Á., Kornai, A.: Subword pooling makes a difference. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 2284–2295. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.eacl-main.194

2. Alshemali, B., Kalita, J.K.: Improving the reliability of deep neural networks in NLP: a review. Knowl. Based Syst. 191, 105210 (2020). https://api.semanticscholar.org/CorpusID:209100013

3. Amara, K., El-Assady, M., Ying, R.: Ginx-eval: towards in-distribution evaluation of graph neural network explanations. arXiv preprint arXiv:2309.16223 (2023)

4. Amara, K., Sevastjanova, R., El-Assady, M.: Syntaxshap: syntax-aware explainability method for text generation. arXiv preprint arXiv:2402.09259 (2024)

5. Arora, A., Jurafsky, D., Potts, C.: Causalgym: benchmarking causal interpretability methods on linguistic tasks. arXiv preprint arXiv:2402.12560 (2024)

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