Papers by Christina Zhang
Reasoning about Uncertainty: Do Reasoning Models Know When They Don’t Know? (2026.findings-eacl)
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| Challenge: | Reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). |
| Approach: | They introduce introspective uncertainty quantification to examine whether reasoning models are well-calibrated and does deeper reasoning improve their calibration? |
| Outcome: | The proposed model calibrations show that models are overconfident, overconfent and overconfust with deeper reasoning. |
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid (2024.findings-emnlp)
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Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth Olson, Henry Dambanemuya, João Bárcia, David Batista, Christina Wille, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Humanitarian organizations can analyze data to discover trends, gather aggregated insights, manage security risks, and inform advocacy and funding proposals. |
| Approach: | They present a dataset comprising news articles in three languages containing instances of different types of violent incidents categorized by the humanitarian sector they impact. |
| Outcome: | The proposed framework can be used to identify violent incidents and identify their impact on humanitarian operations. |
Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding (2022.naacl-main)
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| Challenge: | Desire is a primitive instinct and a need for strongly expressing human desires to get or possess something. |
| Approach: | They propose to use MSED to model and understand human desire . they propose to provide a benchmark for human desire analysis . |
| Outcome: | The proposed dataset contains 9,190 text-image pairs with English text. |