Are Prompt-based Models Clueless?
Author:
Affiliation:
1. Tohoku University
2. LegalForce Research
Publisher
Association for Natural Language Processing
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://www.jstage.jst.go.jp/article/jnlp/29/3/29_991/_pdf
Reference9 articles.
1. Bowman, S. R., Angeli, G., Potts, C., and Manning, C. D. (2015). “A Large Annotated Corpus for Learning Natural Language Inference.” In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 632–642, Lisbon, Portugal. Association for Computational Linguistics.
2. Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., and Smith, N. A. (2018). “Annotation Artifacts in Natural Language Inference Data.” In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 107–112, New Orleans, Louisiana. Association for Computational Linguistics.
3. Kavumba, P., Heinzerling, B., Brassard, A., and Inui, K. (2021). “Learning to Learn to be Right for the Right Reasons.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3890–3898, Online. Association for Computational Linguistics.
4. Kavumba, P., Inoue, N., Heinzerling, B., Singh, K., Reisert, P., and Inui, K. (2019). “When Choosing Plausible Alternatives, Clever Hans can be Clever.” In Proceedings of the 1st Workshop on Commonsense Inference in Natural Language Processing, pp. 33–42, Hong Kong, China. Association for Computational Linguistics.
5. Kavumba, P., Takahashi, R., and Oda, Y. (2022). “Are Prompt-based Models Clueless?” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2333–2352, Dublin, Ireland. Association for Computational Linguistics.
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