Augenstein, Isabelle

Factuality challenges in the era of large language models and opportunities for fact-checking

Augenstein, I., Baldwin, T., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D., DiResta, R., Ferrara, E., Hale, S., Halevy, A., et al. (2024) · Nature Machine Intelligence

Misinformation and Its ImpactsTopic Modeling

Cited by 187

Faithfulness Tests for Natural Language Explanations

Atanasova, P., Camburu, O.-M., Lioma, C., Lukasiewicz, T., Simonsen, J. G., & Augenstein, I. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 69

Disembodied Machine Learning: On the Illusion of Objectivity in NLP

Waseem, Z., Lulz, S., Bingel, J., & Augenstein, I. (2021) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 34

Factuality Challenges in the Era of Large Language Models

Augenstein, I., Baldwin, T., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D., DiResta, R., Ferrara, E., Hale, S., Halevy, A., et al. (2023) · arXiv

Misinformation and Its ImpactsEthics and Social Impacts of AI

Cited by 32

Latent Multi-Task Architecture Learning

Ruder, S., Bingel, J., Augenstein, I., & Søgaard, A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 14

Quantifying gender bias towards politicians in cross-lingual language models

Stańczak, K., Ray Choudhury, S., Pimentel, T., Cotterell, R., & Augenstein, I. (2023) · PLOS ONE

Ethics and Social Impacts of AIGender Studies in Language

Cited by 13

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

Wang, Y., Reddy, R. G., Mujahid, Z. M., Arora, A., Rubashevskii, A., Geng, J., Afzal, O. M., Pan, L., Borenstein, N., Pillai, A., et al. (2023) · arXiv

Misinformation and Its ImpactsTopic Modeling

Cited by 3