Papers by Atoosa Chegini
RePanda: Pandas-powered Tabular Verification and Reasoning (2025.acl-long)
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| Challenge: | Existing methods for fact-checking tabular data rely on black-box models with opaque reasoning. |
| Approach: | They propose a structured fact verification approach that translates claims into executable pandas queries. |
| Outcome: | The proposed method outperforms existing methods and demonstrates strong OOD robustness. |
Reasoning’s Razor: Reasoning Improves Accuracy but Hurts Recall at Critical Operating Points in Safety and Hallucination Detection (2026.eacl-long)
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Atoosa Chegini, Hamid Kazemi, Garrett Souza, Maria Safi, Yang Song, Samy Bengio, Sinead Williamson, Mehrdad Farajtabar
| Challenge: | a new study examines the suitability of reasoning for precision-sensitive classification tasks . false positives carry severe operational consequences, such as blocking legitimate queries . |
| Approach: | They propose to use reasoning for classification tasks under low false positive rate regimes . they find that Think On improves overall accuracy, but performs poorly at low FPRs a . |
| Outcome: | The proposed reasoning-augmented generation model outperforms self-verbalized confidence in precision-sensitive deployments. |