Papers by Dan Vann

3 papers
Anecdoctoring: Automated Red-Teaming Across Language and Place (2025.emnlp-main)

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Challenge: Disinformation is among the top risks of generative AI misuse . red-teaming datasets are typically US- and English-centric .
Approach: They propose a red-teaming approach that generates adversarial prompts across languages and cultures by clustering misinformation claims into broader narratives and enhancing an attacker LLM.
Outcome: The proposed approach produces higher attack success rates and interpretability benefits relative to few-shot prompting.
FairPrism: Evaluating Fairness-Related Harms in Text Generation (2023.acl-long)

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Challenge: FairPrism dataset provides a framework for measuring and mitigating fairness-related harms caused by AI text generation systems.
Approach: They propose a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality.
Outcome: FairPrism is a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering harms relating to gender and sexuality.
Taxonomizing Representational Harms using Speech Act Theory (2025.findings-acl)

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Challenge: a theoretical framework defines representational harms as perlocutionary effects of illocutional acts . the framework provides a granular taxonomy of ils that cause representational damages .
Approach: They propose a framework that defines representational harms as perlocutionary effects of system behaviors . they propose illocutional acts that cause representational damage and a taxonomy that supports measurement instruments .
Outcome: The proposed framework defines representational harms as perlocutionary effects of illocutionaries . it can support the development of valid measurement instruments, the authors show .

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