11 citations · 15 across the 6 of their papers we have counts for
8 papers
Optimal Fusion Weights Form Intervals, Not Points: A Latency-Aware Study of Query-Adaptive WRRF
Iris Ma, Zahra Shekarchi, Stuart Mcilroy +3
Reciprocal Rank Fusion (RRF) is widely used to combine sparse and dense retrievers in hybrid information retrieval, but a critical limitation is that it assigns a fixed, uniform we…
Denoising Iterative Self-Correction: Structured Verification Loops for Reliable LLM Reasoning
Shen Yin, David Ken, Joel Stremmel
Large language models produce fluent but often incorrect multi-step reasoning, and naive correction methods risk degrading already-correct answers. We introduce Denoising Iterative…
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…
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Masoud Monajatipoor, Jiaxin Yang, Joel Stremmel +4
Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedical due to the complexities of language and data sc…
XAIQA: Explainer-Based Data Augmentation for Extractive Question Answering
Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh +4
Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understandin…
Surpassing GPT-4 Medical Coding with a Two-Stage Approach
Zhichao Yang, Sanjit Singh Batra, Joel Stremmel +1
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations. However, the GPT-4 LLM predic…
Extend and Explain: Interpreting Very Long Language Models
Joel Stremmel, Brian L. Hill, Jeffrey Hertzberg +3
While Transformer language models (LMs) are state-of-the-art for information extraction, long text introduces computational challenges requiring suboptimal preprocessing steps or a…
Pretraining Federated Text Models for Next Word Prediction
Joel Stremmel, Arjun Singh
Federated learning is a decentralized approach for training models on distributed devices, by summarizing local changes and sending aggregate parameters from local models to the cl…