Papers by Brenda Curtis
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas (2024.findings-emnlp)
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Salvatore Giorgi, Tingting Liu, Ankit Aich, Kelsey Isman, Garrick Sherman, Zachary Fried, João Sedoc, Lyle Ungar, Brenda Curtis
| Challenge: | Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. |
| Approach: | They propose to prompt LLMs with human-like personas and ask them to answer as if they were a specific human, either explicitly, with exact demographics, political beliefs, and lived experiences, or implicitly via names prevalent in specific populations. |
| Outcome: | The proposed model is based on explicit, explicit, and implicit personas, and fails to show implicit biases. |
Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains where safety depends on how a system answers . out-of-domain (OOD) queries can impair performance and safety . |
| Approach: | They propose to use lightweight, KB-aligned OOD detection as an always-on gate for RAG systems. |
| Outcome: | The proposed method scores queries in a compact subspace selected either by explained-variance retention (EVR) or by a separability-driven -test ranking. |