Societal Biases in Language Generation: Progress and Challenges

Attention Is All You Need

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MizAR 60 for Mizar 50

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Language Models are Few-Shot Learners

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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · cited 1× · see citing works

XLNet: Generalized Autoregressive Pretraining for Language Understanding

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VADER: a parsimonious rule-based model for sentiment analysis of social media text

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Fairness through awareness

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Transformer-XL: Attentive language models beyond a fixed-length context

Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · see citing works

Equality of Opportunity in Supervised Learning

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Datasheets for Datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474

Datasheets for datasets

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works

The Curious Case of Neural Text Degeneration

Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266 · see citing works

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242 · see citing works

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., Gao, J., Liu, J., & Dolan, B. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/2020.acl-demos.30 · Cited by 1,076 · see citing works

Language (Technology) is Power: A Critical Survey of “Bias” in NLP

Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.485 · Cited by 1,050 · cited 3× · see citing works

Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science

Bender, E. M., & Friedman, B. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00041 · Cited by 854 · see citing works

Extracting Training Data from Large Language Models

Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works

Universal Adversarial Triggers for Attacking and Analyzing NLP

Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1221 · Cited by 642 · see citing works

Data and its (dis)contents: A survey of dataset development and use in machine learning research

Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100336 · Cited by 548 · cited 1× · see citing works

Unequal Representation and Gender Stereotypes in Image Search Results for Occupations

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Plug and Play Language Models: A Simple Approach to Controlled Text Generation

Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., & Liu, R. (2019) · arXiv · doi:10.48550/arxiv.1912.02164 · Cited by 508 · see citing works

Evaluating Gender Bias in Machine Translation

Stanovsky, G., Smith, N. A., & Zettlemoyer, L. (2019) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1164 · Cited by 492 · see citing works

Mitigating Gender Bias in Natural Language Processing: Literature Review

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1159 · Cited by 492 · see citing works

Persistent Anti-Muslim Bias in Large Language Models

Abid, A., Farooqi, M., & Zou, J. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462624 · Cited by 476 · see citing works

Untitled work

(2020) · arXiv (Cornell University) · Cited by 461 · see citing works

The Woman Worked as a Babysitter: On Biases in Language Generation

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 431 · cited 10× · see citing works

Assessing gender bias in machine translation: a case study with Google Translate

Prates, M. O. R., Avelar, P. H., & Lamb, L. C. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04144-6 · Cited by 410 · see citing works

Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

Kiritchenko, S., & Mohammad, S. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s18-2005 · Cited by 384 · see citing works

Learning Gender-Neutral Word Embeddings

Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K.-W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1521 · Cited by 376 · see citing works

Reducing Gender Bias in Abusive Language Detection

Park, J. H., Shin, J., & Fung, P. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1302 · Cited by 323 · see citing works

Release Strategies and the Social Impacts of Language Models

Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., et al. (2019) · arXiv · doi:10.48550/arxiv.1908.09203 · Cited by 292 · see citing works

Gender and Representation Bias in GPT-3 Generated Stories

Lucy, L., & Bamman, D. (2021) · Proceedings of the Third Workshop on Narrative Understanding · doi:10.18653/v1/2021.nuse-1.5 · Cited by 287 · see citing works

Gender Bias in Neural Natural Language Processing

Lu, K., Mardziel, P., Wu, F., Amancharla, P., & Datta, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62077-6_14 · Cited by 284 · see citing works

Fairness Without Demographics in Repeated Loss MinimizationPreprint

Hashimoto, T., Srivastava, M., Namkoong, H., & Liang, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08010 · Cited by 263 · see citing works

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

Dhamala, J., Sun, T., Kumar, V., Krishna, S., Pruksachatkun, Y., Chang, K.-W., & Gupta, R. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445924 · Cited by 244 · see citing works

Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology

Zmigrod, R., Mielke, S. J., Wallach, H., & Cotterell, R. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1161 · Cited by 241 · see citing works

Predictive biases in natural language processing models: A conceptual framework and overview

Shah, D. S., Schwartz, H. A., & Hovy, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.468 · Cited by 241 · see citing works

Re-imagining Algorithmic Fairness in India and Beyond

Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., & Prabhakaran, V. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445896 · Cited by 228 · cited 1× · see citing works

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Schick, T., Udupa, S., & Schütze, H. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00434 · Cited by 227 · see citing works

GeDi: Generative Discriminator Guided Sequence Generation

Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., & Rajani, N. F. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.424 · Cited by 220 · see citing works

Identifying and Reducing Gender Bias in Word-Level Language Models

Bordia, S., & Bowman, S. R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop · doi:10.18653/v1/n19-3002 · Cited by 219 · see citing works

Gender Bias in Machine Translation

Savoldi, B., Gaido, M., Bentivogli, L., Negri, M., & Turchi, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00401 · Cited by 189 · see citing works

Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques

Font, J. E., & Costa-jussà, M. (2019) · Proceedings of the First Workshop on Gender Bias in Natural Language Processing · doi:10.18653/v1/w19-3821 · Cited by 187 · see citing works

Ethical Challenges in Data-Driven Dialogue Systems

Henderson, P., Sinha, K., Gontier, N., Ke, N. R., Fried, G., Lowe, R., & Pineau, J. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278777 · Cited by 166 · see citing works

Getting Gender Right in Neural Machine Translation

Vanmassenhove, E., Hardmeier, C., & Way, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1334 · Cited by 158 · see citing works

Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation

Dinan, E., Fan, A., Williams, A., Urbanek, J., Kiela, D., & Weston, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.656 · Cited by 157 · see citing works

Automatically Neutralizing Subjective Bias in Text

Pryzant, R., Martinez, R. D., Dass, N., Kurohashi, S., Jurafsky, D., & Yang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5385 · Cited by 155 · see citing works

Decolonising Speech and Language Technology

Bird, S. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.313 · Cited by 151 · see citing works

Untitled work

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Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models

Tamkin, A., Brundage, M., Clark, J., & Ganguli, D. (2021) · arXiv · doi:10.48550/arxiv.2102.02503 · Cited by 135 · see citing works

Distilling Knowledge Learned in BERT for Text Generation

Chen, Y.-C., Gan, Z., Yu, C., Liu, J., & Liu, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.705 · Cited by 131 · see citing works

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J., Maini, V., Yogatama, D., & Kohli, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.7 · Cited by 124 · see citing works

Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective

Kiritchenko, S., Nejadgholi, I., & Fraser, K. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12590 · Cited by 118 · see citing works

Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem

Saunders, D., & Byrne, B. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.690 · Cited by 100 · see citing works

Dats Wassup!!: Investigating African-American Vernacular English in Transformer-Based Text Generation

Groenwold, S., Ou, L.-H., Parekh, A., Honnavalli, S., Levy, S., Mirza, D., & Wang, W. Y. (2020) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2020.emnlp-main.473 · Cited by 100 · see citing works

Towards Controllable Biases in Language Generation

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.291 · Cited by 95 · see citing works

HONEST: Measuring Hurtful Sentence Completion in Language Models

Nozza, D., Bianchi, F., & Hovy, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.191 · Cited by 95 · see citing works

Multi-Dimensional Gender Bias Classification

Dinan, E., Fan, A., Wu, L., Weston, J., Kiela, D., & Williams, A. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.23 · Cited by 94 · see citing works

Does Gender Matter? Towards Fairness in Dialogue Systems

Liu, H., Dacon, J., Fan, W., Liu, H., Liu, Z., & Tang, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.390 · Cited by 81 · see citing works

GeDi: Generative Discriminator Guided Sequence GenerationPreprint

Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., & Rajani, N. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.06367 · Cited by 72 · see citing works

Mitigating Gender Bias in Machine Translation with Target Gender AnnotationsPreprint

Stafanovičs, A., Pinnis, M., & Bergmanis, T. (2020) · Conference on Machine Translation · doi:10.48550/arxiv.2010.06203 · Cited by 71 · see citing works

Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function

Qian, Y., Muaz, U., Zhang, B., & Hyun, J. W. (2019) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/p19-2031 · Cited by 63 · see citing works

PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction

Ma, X., Sap, M., Rashkin, H., & Choi, Y. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.602 · Cited by 61 · see citing works

Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

Liu, H., Wang, W., Wang, Y., Liu, H., Liu, Z., & Tang, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.64 · Cited by 57 · see citing works

On Measuring Gender Bias in Translation of Gender-neutral Pronouns

Cho, W. I., Kim, J. W., Kim, S., & Kim, N. S. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3824 · Cited by 56 · see citing works

The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing

Tomalin, M., Byrne, B., Concannon, S. J., Saunders, D., & Ullmann, S. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09583-1 · Cited by 55 · see citing works

“You Sound Just Like Your Father” Commercial Machine Translation Systems Include Stylistic Biases

Hovy, D., Bianchi, F., & Fornaciari, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.154 · Cited by 47 · see citing works

Towards a comprehensive understanding and accurate evaluation of societal biases in pre-trained transformers

Silva, A., Tambwekar, P., & Gombolay, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.189 · Cited by 46 · see citing works

They, Them, Theirs: Rewriting with Gender-Neutral EnglishPreprint

Sun, T., Webster, K., Shah, A., Wang, W. Y., & Johnson, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06788 · Cited by 42 · see citing works

“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.60 · Cited by 42 · see citing works

“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models

Shwartz, V., Rudinger, R., & Tafjord, O. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.556 · Cited by 41 · see citing works

Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make ItPreprint

Saunders, D., Sallis, R., & Byrne, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.05332 · Cited by 41 · see citing works

Automatic Gender Identification and Reinflection in Arabic

Habash, N. Y., Bouamor, H., & Chung, C. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3822 · Cited by 35 · see citing works

Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection

Moryossef, A., Aharoni, R., & Goldberg, Y. (2019) · Workshop on Gender Bias in Natural Language Processing (GeBNLP) · doi:10.18653/v1/w19-3807 · Cited by 35 · see citing works

Mitigating Gender Bias in Machine Translation with Target Gender Annotations

Stafanovičs, A., Bergmanis, T., & Pinnis, M. (2020) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2020.wmt-1.73 · Cited by 35 · see citing works

Discovering and Categorising Language Biases in Reddit

Ferrer, X., van Nuenen, T., Such, J. M., & Criado, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18048 · Cited by 34 · see citing works

Gender aware spoken language translation applied to English-Arabic

ElAraby, M., Tawfik, A. Y., Khaled, M., Hassan, H., & Osama, A. (2018) · International Conference on Natural Language and Speech Processing (ICNLSP) · doi:10.1109/icnlsp.2018.8374387 · Cited by 29 · see citing works

Automatically Identifying Gender Issues in Machine Translation using Perturbations

Gonen, H., & Webster, K. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.180 · Cited by 28 · see citing works

Investigating Failures of Automatic Translation in the Case of Unambiguous GenderPreprint

Renduchintala, A., & Williams, A. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2104.07838 · Cited by 28 · see citing works

Investigating Societal Biases in a Poetry Composition SystemPreprint

Sheng, E., & Uthus, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.02686 · Cited by 26 · see citing works

Reducing Non-Normative Text Generation from Language Models

Peng, X., Li, S., Frazier, S., & Riedl, M. O. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.43 · Cited by 25 · see citing works

Fine-tuning neural machine translation on gender-balanced datasets

Costa‐jussà, M. R., & Sánchez, A. D. J. (2020) · Cited by 24 · see citing works

Towards Cross-Lingual Generalization of Translation Gender Bias

Cho, W. I., Kim, J., Yang, J., & Kim, N. S. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445907 · Cited by 23 · see citing works

Revealing Persona Biases in Dialogue SystemsPreprint

Sheng, E., Arnold, J., Yu, Z., Chang, K.-W., & Peng, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08728 · Cited by 21 · see citing works

Defining and Evaluating Fair Natural Language Generation

Yeo, C., & Chen, A. (2020) · doi:10.18653/v1/2020.winlp-1.27 · Cited by 20 · see citing works

Decoding and Diversity in Machine TranslationPreprint

Roberts, N. P., Liang, D., Neubig, G., & Lipton, Z. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13477 · Cited by 20 · see citing works

How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points

Farkas, A., & Németh, R. (2021) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2021.100239 · Cited by 20 · see citing works

Gender-Aware Reinflectionusing Linguistically Enhanced Neural Models

International Committee for Computational Linguistics 2020, Alhafni, B., Bouamor, H., & Habash, N. (2020) · doi:10.48448/6nn5-6v05 · Cited by 20 · see citing works

Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information

Basta, C., Costa‐jussà, M. R., & Fonollosa, J. A. R. (2020) · Widening Natural Language Processing Workshop (WiNLP) · doi:10.18653/v1/2020.winlp-1.25 · Cited by 19 · see citing works

Conversational Assistants and Gender Stereotypes: Public Perceptions and Desiderata for Voice Personas

Curry, A. C., Robertson, J., & Rieser, V. (2020) · Cited by 17 · see citing works

Persistent Anti-Muslim Bias in Large Language ModelsPreprint

Abid, A., Farooqi, M., & Zou, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.05783 · Cited by 17 · see citing works

First the Worst: Finding Better Gender Translations During Beam Search

Saunders, D., Sallis, R., & Byrne, B. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.301 · Cited by 16 · see citing works

Gender Coreference and Bias Evaluation at WMT 2020

Kocmi, T., Limisiewicz, T., & Stanovsky, G. (2020) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2020.wmt-1.39 · Cited by 14 · see citing works

Improving Gender Translation Accuracy with Filtered Self-TrainingPreprint

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On-the-Fly Controlled Text Generation with Experts and Anti-Experts.Preprint

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Gender Bias in Multilingual Neural Machine Translation: The Architecture MattersPreprint

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Reducing Non-Normative Text Generation from Language ModelsPreprint

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Discovering and Categorising Language Biases in RedditPreprint

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Gender Bias in Multilingual Neural Machine Translation: The Architecture MattersPreprint

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How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases.Preprint

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The Truth is Out There: Investigating Conspiracy Theories in Text Generation.Preprint

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Dialect Diversity in Text Summarization on TwitterPreprint

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Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

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First the worst: Finding better gender translations during beam searchPreprint

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Dialect Diversity in Text Summarization on TwitterPreprint

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A Survey on Evaluation of Large Language Models

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PaLM: Scaling Language Modeling with Pathways

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Bias and Fairness in Large Language Models: A Survey

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Can Large Language Models Transform Computational Social Science?

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Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends

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Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health Intervention

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"I wouldn't say offensive but...": Disability-Centered Perspectives on Large Language Models

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“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters

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Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies

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The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships

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Addressing 6 challenges in generative AI for digital health: A scoping review

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Language Model Behavior: A Comprehensive Survey

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History, development, and principles of large language models: an introductory survey

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The Security of Using Large Language Models: A Survey with Emphasis on ChatGPT

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The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective

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“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset

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Are LLMs good at structured outputs? A benchmark for evaluating structured output capabilities in LLMs

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Cultural Bias in Large Language Models: A Comprehensive Analysis and Mitigation Strategies

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Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics

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Quantifying and alleviating political bias in language models

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You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings

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FLUTE: Figurative Language Understanding through Textual Explanations

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Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models

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“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation

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Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

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A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs’ Humour Alignment with Comedians

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Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark

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Pipelines for Social Bias Testing of Large Language Models

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SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control

Han, X., Kumar, S., & Tsvetkov, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.647 · Cited by 41 · see citing works

AI-Generated Text Detector for Arabic Language Using Encoder-Based Transformer Architecture

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Evaluation of African American Language Bias in Natural Language Generation

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Simulating Climate Change Discussion with Large Language Models: Considerations for Science Communication at Scale

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Inspecting the Geographical Representativeness of Images from Text-to-Image Models

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Misrepresentation or inclusion: promises of generative artificial intelligence in climate change education

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Exploring the prospects of multimodal large language models for Automated Emotion Recognition in education: Insights from Gemini

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Challenging Fairness: A Comprehensive Exploration of Bias in LLM-Based Recommendations

Sakib, S. K., & Das, A. B. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825082 · Cited by 17 · see citing works

Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications

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Toward fair medical advice: Addressing and mitigating bias in large language model-based healthcare applications

Lu, H., Lin, Y., Li, Z., Yiu, M. L., Gao, Y., & Uddin, S. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103216 · Cited by 16 · see citing works

Mitigating social biases of pre-trained language models via contrastive self-debiasing with double data augmentation

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Toward Robust Arabic AI-Generated Text Detection: Tackling Diacritics Challenges

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“Cold, Calculated, and Condescending”: How AI Identifies and Explains Ableism Compared to Disabled People

Phutane, M., Seelam, A., & Vashistha, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732128 · Cited by 14 · see citing works

Towards realistic evaluation of cultural value alignment in large language models: Diversity enhancement for survey response simulation

Liu, H., Cao, Y., Wu, X., Qiu, C., Gu, J., Liu, M., & Hershcovich, D. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104099 · Cited by 14 · see citing works

Exploring biases related to the use of large language models in a multilingual depression corpus

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LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting

Ramos, R., Martins, B., & Elliott, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.104 · Cited by 17 · see citing works

Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias

Vamvas, J., & Sennrich, R. (2021) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.803 · Cited by 27 · see citing works

CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models

Zhao, J., Fang, M., Shi, Z., Li, Y., Chen, L., & Pechenizkiy, M. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.757 · Cited by 14 · see citing works

Detecting Gender Bias in Transformer-based Models: A Case Study on BERTPreprint

Li, B., Peng, H., Sainju, R., Yang, J., Yang, L., Liang, Y., Jiang, W., Wang, B., Liu, H., & Ding, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15733 · Cited by 15 · see citing works

Which Stereotypes Are Moderated and Under-Moderated in Search Engine Autocompletion?

Leidinger, A., & Rogers, R. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594062 · Cited by 12 · see citing works

Exploring Social Biases of Large Language Models in a College Artificial Intelligence Course

Kolisko, S., & Anderson, C. J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26879 · Cited by 11 · see citing works

Conversational User Interfaces and Agents

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Application of Artificial Intelligence and Virtual Reality in Soft Skills Training with Modeled Personality

Budnarowski, D., Jereczek, D., Detka, K., & Wieczorek, I. (2025) · Applied Sciences · doi:10.3390/app15169067 · Cited by 20 · see citing works

Religious bias landscape in language and text-to-image models: analysis, detection, and debiasing strategies

Abrar, A., Oeshy, N. T., Kabir, M., & Ananiadou, S. (2025) · AI & Society · doi:10.1007/s00146-025-02721-z · Cited by 15 · see citing works

Culturally Aware Natural Language Inference

Huang, J., & Yang, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.509 · Cited by 10 · see citing works

Evaluating Synthetic Data Generation from User Generated Text

Chim, J., Ive, J., & Liakata, M. (2024) · Computational Linguistics · doi:10.1162/coli_a_00540 · Cited by 10 · see citing works

Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Model

Amrhein, C., Schottmann, F., Sennrich, R., & Läubli, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.246 · Cited by 10 · see citing works

Integrating Artificial Intelligence in Medical Writing: Balancing Technological Innovation and Human Expertise, with Practical Applications in Lower Extremity Wounds Care

Thaichana, P., Oo, M. Z., Thorup, G. L., Chansakaow, C., Arworn, S., & Rerkasem, K. (2025) · The International Journal of Lower Extremity Wounds · doi:10.1177/15347346241312814 · Cited by 13 · see citing works

Embracing large language model (LLM) technologies in hydrology research

Ma, Z., Peng, B., Yue, Z., Zeng, H., Pan, M., Wu, X., Yang, J., Mai, L., & Guan, K. (2025) · Environmental Research Water · doi:10.1088/3033-4942/addd43 · Cited by 10 · see citing works

Evaluating Evaluation Metrics: A Framework for Analyzing NLG Evaluation Metrics using Measurement Theory

Xiao, Z., Zhang, S., Lai, V. W.-M., & Liao, Q. V. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.676 · Cited by 17 · see citing works

A Framework for Auditing Chatbots for Dialect-Based Quality-of-Service Harms

Harvey, E., Kizilcec, R. F., & Koenecke, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732137 · Cited by 8 · see citing works

Towards Robust NLG Bias Evaluation with Syntactically-diverse Prompts

Aggarwal, A., Sun, J., & Peng, N. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.445 · Cited by 8 · see citing works

Conditional Supervised Contrastive Learning for Fair Text Classification

Chi, J., Shand, W., Yu, Y., Chang, K.-W., Zhao, H., & Tian, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.199 · Cited by 8 · see citing works

Speciesist language and nonhuman animal bias in English Masked Language Models

Takeshita, M., Rzepka, R., & Araki, K. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103050 · Cited by 10 · see citing works

The tail wagging the dog: Dataset construction biases of social bias benchmarks

Selvam, N., Dev, S., Khashabi, D., Khot, T., & Chang, K.-W. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2023.acl-short.118 · Cited by 11 · see citing works

Controlling Bias Exposure for Fair Interpretable Predictions

He, Z., Wang, Y., McAuley, J., & Majumder, B. P. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.431 · Cited by 8 · see citing works

Detecting and Measuring Social Bias of Arabic Generative Models in the Context of Search and Recommendation

Harrag, F., Mahdadi, C., & Ziad, A. N. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37249-0_13 · Cited by 7 · see citing works

S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models

Yuan, X., Li, J., Wang, D., Chen, Y., Mao, X., Huang, L., Chen, J., Xue, H., Liu, X., Wang, W., et al. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728971 · Cited by 27 · see citing works

Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness

Mireshghallah, F., & Berg-Kirkpatrick, T. (2021) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.152 · Cited by 20 · see citing works

Towards Better Detection of Biased Language with Scarce, Noisy, and Biased Annotations

Li, Z., Lu, Z., & Yin, M. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534142 · Cited by 7 · see citing works

A Multi-dimensional study on Bias in Vision-Language models

Ruggeri, G., & Nozza, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.403 · Cited by 7 · see citing works

DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domainsPreprint

Labrak, Y., Bazoge, A., Dufour, R., Rouvier, M., Morin, E., Daille, B., & Gourraud, P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.03.535368 · Cited by 7 · see citing works

An Analysis of The Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation

Dhamala, J., Kumar, V., Gupta, R., Chang, K.-W., & Galstyan, A. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022722 · Cited by 7 · see citing works

CONSISTENT: Open-Ended Question Generation From News Articles

Chakrabarty, T., Lewis, J., & Muresan, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.517 · Cited by 6 · see citing works

Explicit Syntactic Guidance for Neural Text Generation

Li, Y., Cui, L., Yan, J., Yin, Y., Bi, W., Shi, S., & Zhang, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.788 · Cited by 8 · see citing works

SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created through Human-Machine Collaboration

Lee, H., Hong, S., Park, J., Kim, T., Cha, M., Choi, Y., Kim, B., Kim, G., Lee, E., Lim, Y., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.370 · Cited by 5 · see citing works

Detecting intersectionality in NER models: A data-driven approach

Lassen, I. M. S., Almasi, M., Enevoldsen, K., & Kristensen‐McLachlan, R. D. (2023) · Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL) · doi:10.18653/v1/2023.latechclfl-1.13 · Cited by 5 · see citing works

In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages

Ersoy, A., Vizcarra, G., Mayeesha, T. T., & Müller, B. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.175 · Cited by 5 · see citing works

Robust Natural Language Understanding with Residual Attention Debiasing

Wang, F., Huang, J. Y., Yan, T., Zhou, W., & Chen, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.32 · Cited by 5 · see citing works

What do Bias Measures MeasurePreprint

Dev, S., Sheng, E., Zhao, J., Sun, J., Hou, Y., Sanseverino, M., Kim, J., Peng, N., & Chang, K.-W. (2021) · arXiv (Cornell University) · Cited by 6 · see citing works

The State of Profanity Obfuscation in Natural Language Processing Scientific Publications

Nozza, D., & Hovy, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.240 · Cited by 4 · see citing works

An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models

Hosseini, S., Palangi, H., & Awadallah, A. H. (2023) · Workshop on Trustworthy Natural Language Processing (TrustNLP) · doi:10.18653/v1/2023.trustnlp-1.11 · Cited by 7 · see citing works

Hi Guys or Hi Folks? Benchmarking Gender-Neutral Machine Translation with the GeNTE Corpus

Piergentili, A., Savoldi, B., Fucci, D., Negri, M., & Bentivogli, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.873 · Cited by 6 · see citing works

AUTOMATIC QUESTION GENERATION THROUGH WORD VECTOR SYNCHRONIZATION USING LAMMA

Khandait, K., Bhura, S. A., & Asole, S. S. (2022) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2022/v13i4/221304046 · Cited by 4 · see citing works

Behavior Cloned Transformers are Neurosymbolic Reasoners

Wang, R., Jansen, P., Côté, M.-A., & Ammanabrolu, P. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.204 · Cited by 4 · see citing works

A Survey of Hallucination in Large Language Models

何, 金. (2026) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2026.151016 · Cited by 3 · see citing works

Societal Implications of Recommendation Systems: A Technical Perspective

Gonçalves-Sá, J., & Pinheiro, F. L. (2023) · Law, governance and technology series · doi:10.1007/978-3-031-41264-6_3 · Cited by 3 · see citing works

Bilingual generated text detection through semantic and statistical analysis

Min, C., Zhang, R., & Liu, J. (2025) · Intelligent Data Analysis · doi:10.1177/1088467x241307192 · Cited by 3 · see citing works

Controlled Language Generation for Language Learning Items

Stowe, K., Ghosh, D., & Zhao, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.30 · Cited by 3 · see citing works

Saliency Guided Debiasing: Detecting and mitigating biases in LMs using feature attribution

Joshi, R. K., Chatterjee, A., & Ekbal, A. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126851 · Cited by 3 · see citing works

Towards comparable ratings: Exploring bias in German physician reviews

Kersting, J., Maoro, F., & Geierhos, M. (2023) · Data & Knowledge Engineering · doi:10.1016/j.datak.2023.102235 · Cited by 3 · see citing works

Geographical Erasure in Language Generation

Schwöbel, P., Golebiowski, J., Donini, M., Archambeau, C., & Pruthi, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.823 · Cited by 5 · see citing works

A multifaceted approach to detect gender biases in Natural Language Generation

Consuegra-Ayala, J. P., Martínez-Murillo, I., Lloret, E., Moreda, P., & Palomar, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112367 · Cited by 1 · see citing works

Are Male Candidates Better than Females? Debiasing BERT Resume Retrieval System

Zhang, S., Li, P., & Cai, Z. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945184 · Cited by 3 · see citing works

Human-Centered AI for Migrant Integration Through LLM and RAG Optimization

Castellanos–Nieves, D., & García-Forte, L. (2024) · Applied Sciences · doi:10.3390/app15010325 · Cited by 2 · see citing works

Guiding the Next Technological Revolution

Gutierrez, R. (2023) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-0368-9.ch011 · Cited by 3 · see citing works

Entity-Based Evaluation of Political Bias in Automatic Summarization

Zhou, K., & Tan, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.696 · Cited by 5 · see citing works

Understanding and Mitigating Gender Bias in Information Retrieval Systems

Seyedsalehi, S., Bigdeli, A., Arabzadeh, N., AlMousawi, B., Marshall, Z., Zihayat, M., & Ebrahim Bagheri, A. (2025) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000103 · Cited by 3 · see citing works

Evaluation of Quality of Slovak Language Use in LLMS

Dobeš, M. (2025) · Acta Electrotechnica et Informatica · doi:10.2478/aei-2025-0004 · Cited by 2 · see citing works

Ensemble Transfer Learning for Multilingual Coreference Resolution

Lai, T., & Ji, H. (2023) · Workshop on Computational Approaches to Discourse (CODI) · doi:10.18653/v1/2023.codi-1.3 · Cited by 3 · see citing works

COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation

Wang, N., Wang, Q., Wang, Y.-C., Sanjabi, M., Liu, J., Firooz, H., Wang, H., & Nie, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.819 · Cited by 2 · see citing works

Evaluating the Effects of Prompt Perturbation on Bias and Hallucination in Large Language Models

Yousefi, M., Shahi, A., Sharifi, M., Romera, A. J. J., Hoermann, S., & Piumsomboon, T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6588-4_25 · Cited by 1 · see citing works

The Woman Worked as a Babysitter: On Biases in Language Generation

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1339 · Cited by 431 · see citing works

A Natural Bias for Language Generation Models

Meister, C., Stokowiec, W., Pimentel, T., Yu, L., Rimell, L., & Kuncoro, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.22 · Cited by 2 · see citing works

\nExploring social biases in language processing

Iacozza, S. (2020) · Radboud Repository (Radboud University)

On the Unintended Social Bias of Training Language Generation Models with News Articles

Florez, O. U. (2019)

He is very intelligent, she is very beautiful? On Mitigating Social Biases in Language Modelling and Generation

Garimella, A., Amarnath, A., KUMAR, K. T. K., Yalla, A. P., Natarajan, A., Chhaya, N., & Srinivasan, B. V. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.397 · Cited by 33 · see citing works