8 citations · 32 across the 43 of their papers we have counts for
10 papers · 1 filter
DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text Generation
Miriam Wanner, Benjamin Van Durme, Mark Dredze
The decompose-then-verify strategy for verification of Large Language Model (LLM) generations decomposes claims that are then independently verified. Decontextualization augments t…
Are Clinical T5 Models Better for Clinical Text?
Yahan Li, Keith Harrigian, Ayah Zirikly +1
Large language models with a transformer-based encoder/decoder architecture, such as T5, have become standard platforms for supervised tasks. To bring these technologies to the cli…
Give me Some Hard Questions: Synthetic Data Generation for Clinical QA
Fan Bai, Keith Harrigian, Joel Stremmel +3
Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs). However, training these systems requires signif…
Making FETCH! Happen: Finding Emergent Dog Whistles Through Common Habitats
Kuleen Sasse, Carlos Aguirre, Isabel Cachola +2
WARNING: This paper contains content that maybe upsetting or offensive to some readers. Dog whistles are coded expressions with dual meanings: one intended for the general public (…
Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts
Sharon Levy, William D. Adler, Tahilin Sanchez Karver +2
Large language models (LLMs) acquire beliefs about gender from training data and can therefore generate text with stereotypical gender attitudes. Prior studies have demonstrated mo…
Can Optimization Trajectories Explain Multi-Task Transfer?
David Mueller, Mark Dredze, Nicholas Andrews
Despite the widespread adoption of multi-task training in deep learning, little is understood about how multi-task learning (MTL) affects generalization. Prior work has conjectured…
Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
Kaiser Sun, Mark Dredze
The development of large language models leads to the formation of a pre-train-then-align paradigm, in which the model is typically pre-trained on a large text corpus and undergoes…
A Closer Look at Claim Decomposition
Miriam Wanner, Seth Ebner, Zhengping Jiang +2
As generated text becomes more commonplace, it is increasingly important to evaluate how well-supported such text is by external knowledge sources. Many approaches for evaluating t…
Evaluating Biases in Context-Dependent Health Questions
Sharon Levy, Tahilin Sanchez Karver, William D. Adler +2
Chat-based large language models have the opportunity to empower individuals lacking high-quality healthcare access to receive personalized information across a variety of topics.…
Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
Hanjie Chen, Zhouxiang Fang, Yash Singla +1
LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations. However, medical board exams or ge…