Papers by Lucille Njoo
TalkUp: Paving the Way for Understanding Empowering Language (2023.findings-emnlp)
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| Challenge: | Empowerment has rarely been studied in NLP because of its implicit nature . linguistics and psychology research shows how empowerment can impact people by increasing their sense of self-efficacy and self-esteem. |
| Approach: | They crowdsource Reddit posts labeled for empowerment and use it to train language models that capture empowering and disempowering language. |
| Outcome: | The proposed dataset can be used to train language models that capture empowering and disempowering language. |
Mitigating Societal Harms in Large Language Models (2023.emnlp-tutorial)
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| Challenge: | Recent studies have highlighted societal harms that can be caused by language generation models deployed in the wild. |
| Approach: | They propose to use a typology of technical approaches to mitigating harms of language generation models to provide an overview of potential social issues in language generation including toxicity, social biases, misinformation, factual inconsistency, and privacy violations. |
| Outcome: | The proposed typology addresses toxicity, biases, misinformation, factual inconsistency, and privacy violations in language generation models. |
Gendered Mental Health Stigma in Masked Language Models (2022.emnlp-main)
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Inna Lin, Lucille Njoo, Anjalie Field, Ashish Sharma, Katharina Reinecke, Tim Althoff, Yulia Tsvetkov
| Challenge: | Mental health stigma prevents many individuals from receiving appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. |
| Approach: | They propose to use clinical psychology literature to curate prompts, then evaluate models’ propensity to generate gendered words. |
| Outcome: | The proposed framework captures stigma about gender in mental health and is more likely to predict female subjects than male in sentences about mental health conditions (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. |
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2023.eacl-main)
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| Challenge: | Recent advances in the capacity of large language models to generate human-like text have prompted a heated discourse around the risks of societal harms they introduce. |
| Approach: | They propose a taxonomy of interventions organized around the different phases where they can be adopted to mitigate harms. |
| Outcome: | The proposed methods are based on several prior works’ taxonomies of language model risks and provide an overview of strategies for detecting and ameliorating different kinds of risks/harms. |