Papers by Sarah Preum
Statistical Depth for Ranking and Characterizing Transformer-Based Text Embeddings (2023.emnlp-main)
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| Challenge: | Generalized transformer-based text embedding models have produced state of the art performance results on a variety of tasks such as natural language inference (NLI) |
| Approach: | They propose a statistical depth to measure distributions of transformer-based text embeddings and an associated rank sum test to characterize distributions in synthetic and human-generated corpora. |
| Outcome: | The proposed method improves performance over baseline methods on six text classification tasks. |
Follow-up Question Generation For Enhanced Patient-Provider Conversations (2025.acl-long)
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Joseph Gatto, Parker Seegmiller, Timothy E. Burdick, Inas S. Khayal, Sarah DeLozier, Sarah Masud Preum
| Challenge: | Follow-up question generation is an essential feature of dialogue systems as it can reduce conversational ambiguity and enhance modeling complex interactions. |
| Approach: | They propose a framework that generates personalized follow-up questions based on patient utterances and prior EHR data. |
| Outcome: | The framework reduces follow-up communications by 34% and improves performance by 17% and 5% on real and synthetic data. |
Chain-of-Thought Embeddings for Stance Detection on Social Media (2023.findings-emnlp)
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| Challenge: | Stance detection on social media platforms like Twitter is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit stance labels. |
| Approach: | They propose to embed COT reasonings into a traditional RoBERTa-based stance detection pipeline by embedding COT stance reasonings and integrating them into slang-based models. |
| Outcome: | The proposed model achieves SOTA performance on multiple stance detection datasets collected from social media. |