Papers by Saadia Gabriel
Discourse Understanding and Factual Consistency in Abstractive Summarization (2021.eacl-main)
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Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz, Yejin Choi
| Challenge: | Existing abstractive summarization models often hallucinate information or generate factually incorrect summaries. |
| Approach: | They propose a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. |
| Outcome: | The proposed framework generates abstracts with factual consistency and coherence significantly better than baselines. |
MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (N19-1)
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| Challenge: | Existing datasets in this domain do not offer precise operational annotations over diverse problem types due to noise and lack of formal operation-based representations. |
| Approach: | They propose a representation language to map problems to their operation programs . they also introduce an interpretable neural math problem solver . |
| Outcome: | The proposed model outperforms baseline models and the AQUA-RAT dataset on the AQuA-rat dataset. |
The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP (2026.acl-long)
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Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye, Zhaoyi Zhang, Nicte Aguillon Jimenez, Evans Kofi Agyei, Abraham Lin, Rohan Ramachandran, Sadick Abdul Mumin, Faith Nchifor, Mohammed Shuraim Issah, Erick Rosas Gonzalez, Lieqi Liu, Sylvester Kpei, Jemimah Kusi Osei, Carlene Ajeneza, Persis Boateng, Prisca Adwoa Dufie Yeboah, Saadia Gabriel
| Challenge: | Among the approximately 7,000 languages spoken globally, fewer than 20 receive substantial attention in NLP research. |
| Approach: | They propose to use African multi-modal speech and text data to validate African multimodal models and validate them on targeted language data. |
| Outcome: | The African Languages Lab's results show that the proposed model outperforms untrained models in 31 languages and a 1B-parameter model beats the commercial system in Yoruba and Twi. |
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022.acl-long)
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| Challenge: | Toxic language detection systems often falsely flag text that contains minority group mentions as toxic . this over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. |
| Approach: | They develop a machine-generated dataset of toxic and benign statements about 13 minority groups that generates subtly toxic and harmless text with a massive pretrained language model. |
| Outcome: | The proposed method can detect toxic and benign statements on a large scale . it can also detect hate speech on 94.5% of the toxic examples . |
ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)
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Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
| Challenge: | Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth. |
| Approach: | They propose to augment social media posts with conversational scenarios to reflect the impact of conversational context on toxicity. |
| Outcome: | The proposed model outperforms existing models on social media with conversational scenarios. |
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models (2024.findings-emnlp)
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Jaeyoung Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel
| Challenge: | a growing influx of misinformation across news and social media is hampered by outdated foundation model training data. |
| Approach: | They propose to use large language models to scale up online policing mechanisms . they evaluate foundation model performance without continual updating . |
| Outcome: | The proposed model can improve performance without continual updating . the proposed model improves on two widely used benchmarks . |
The Risk of Racial Bias in Hate Speech Detection (P19-1)
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| Challenge: | Annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. |
| Approach: | They propose *dialect* and *race priming* as ways to reduce the racial bias in hate speech detection models by detecting differences in dialects in annotated tweets. |
| Outcome: | The proposed models acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. |
Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (2022.acl-long)
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Saadia Gabriel, Skyler Hallinan, Maarten Sap, Pemi Nguyen, Franziska Roesner, Eunsol Choi, Yejin Choi
| Challenge: | Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers’ reactions to previously unseen news headlines. |
| Approach: | They propose a pragmatic formalism for modeling how readers might react to a news headline . they propose 'misinfo' frames, which can be used to model reader perceptions of news reliability . |
| Outcome: | The proposed model can predict readers' reactions to previously unseen headlines. |
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations (2025.emnlp-main)
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| Challenge: | generative language agents predict user behaviors such as liking, sharing, and flagging content. |
| Approach: | They propose a framework where generative language agents predict user behaviors such as liking, sharing, and flagging content. |
| Outcome: | The proposed framework analyzes content moderation strategies and user engagement dynamics at scale and demonstrates that agents’ articulated reasoning for their social interactions aligns with their collective engagement patterns. |
MisinfoEval: Generative AI in the Era of “Alternative Facts” (2024.emnlp-main)
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| Challenge: | Existing efforts to address misinformation on social media platforms are hampered by user biases and scalability challenges. |
| Approach: | They propose a framework for generating and comprehensively evaluating large language model based misinformation interventions using a simulated social media environment and personalized explanations tailored to users' beliefs. |
| Outcome: | The proposed framework improves accuracy at reliability labeling by up to 41.72% and personalized explanations appeal to users' pre-existing values. |
Mind the Gesture: Evaluating AI Sensitivity to Culturally Offensive Non-Verbal Gestures (2025.acl-long)
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| Challenge: | a dataset of 288 gesture-country pairs is used to evaluate AI systems' cultural awareness of offensive gestures and nonverbal signs. |
| Approach: | They use a dataset of 288 gesture-country pairs annotated for offensiveness, cultural significance, and contextual factors across 25 gestures and 85 countries. |
| Outcome: | The proposed dataset analyzes 288 gesture-country pairs across 25 gestures and 85 countries. |
NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries? (2022.findings-emnlp)
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| Challenge: | Existing work has explored adversarial example generation for natural language understanding tasks, but these examples are often unrealistic and diverge from the real-world data distributions. |
| Approach: | They propose a framework for adversarial example generation that is effective at fooling a given classifier and a generative model based on the key tokens from the first stage. |
| Outcome: | The proposed framework generalizes across domains and offers insights for future research on improving robustness of neural text classification models. |
Social Bias Frames: Reasoning about Social and Power Implications of Language (2020.acl-main)
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| Challenge: | Language has enormous power to project social biases and reinforce stereotypes on people. |
| Approach: | They propose a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and power differentials onto others. |
| Outcome: | The proposed model can model the pragmatic frames in which people project social biases and power differentials onto others. |
GO FIGURE: A Meta Evaluation of Factuality in Summarization (2021.findings-acl)
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| Challenge: | Recent advances in text generation systems produce fluent, coherent, relevant, and factually correct text. |
| Approach: | They propose a metaevaluation framework for evaluating factuality evaluation metrics . they propose five necessary conditions to evaluate factual metrics on diagnostic factuity data . |
| Outcome: | The proposed framework provides robust evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. |
Can AI Relate: Testing Large Language Model Response for Mental Health Support (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) are already being piloted for clinical use in hospitals . recent failures of the Tessa chatbot have led to doubts about their reliability in high-stakes settings. |
| Approach: | They propose safety guidelines for the potential deployment of large language models for mental health response. |
| Outcome: | The proposed framework measures equity in empathy and adherence of LLM responses to motivational interviewing theory. |