Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation

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P. Atanasova, J. G. Simonsen, C. Lioma, and I. Augenstein, “Generating Fact Checking Explanations,” in Annual Meeting of the Association for Computational Linguistics (ACL) , 2020

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Cited alongside, same era.

A. Marasovic, I. Beltagy, D. Downey, and M. Peters, “Few-shot self-rationalization with natural language prompts,” in Findings of the Association for Computational Linguistics: NAACL 2022 , 2022

2022

Cited alongside, same era.

Y. Yordanov, V. Kocijan, T. Lukasiewicz, and O.-M. Camburu, “Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup,” in Findings of the Association for Computational Linguistics: EMNLP 2022 , 2022

2022

Cited alongside, same era.

D. Russo, S. S. Tekiroğlu, and M. Guerini, “Benchmarking the Generation of Fact Checking Explanations,” Trans. of the Association for Computational Linguistics , vol. 11, pp. 1250–1264, 2023

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Cited alongside, same era.

J. Li, S. Sun, W. Yuan, R.-Z. Fan, P. Liu et al. , “Generative Judge for Evaluating Alignment,” in Intl. Conf. on Learning Representations (ICLR) , 2023

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M. Schlichtkrull, Z. Guo, and A. Vlachos, “Averitec: A dataset for real-world claim verification with evidence from the web,” Advances in Neural Information Processing Systems (NeurIPS) , 2024

2024

Closest in time.

Anonymous, “Self-Rationalization in the Wild: A Large Scale Out-of-Distribution Evaluation on NLI-related tasks,” https://openreview.net/forum?id=KYEdQdGvAR , 2024, preprint available at OpenReview

2024

Closest in time.

M. Zarharan, P. Wullschleger, B. Behkam Kia, M. T. Pilehvar, and J. Foster, “Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models,” in Workshop on Trustworthy Natural Language Processing (TrustNLP 2024) , 2024

2024

Closest in time.