1 citations · 1 across the 4 of their papers we have counts for
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From Noisy Retrieval to Decision Evidence: Silver Evidence Demonstrations for In-Context Learning
Masoud Monajatipoor, Fazlolah Mohaghegh, Hamid Reza Hassanzadeh +2
RAG and retrieval-augmented few-shot ICL can struggle when decision-relevant evidence is buried in long, noisy context. Retrieved passages and demonstrations may contain redundant…
Verb-ICL: Rethinking In-Context Learning for Structured Prediction
Fan Bai, Hengshuo Miao, Sanjit S Batra +3
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-leve…
Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation
Aashish Anantha Ramakrishnan, Ardavan Saeedi, Hamid Reza Hassanzadeh +2
With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as he…
LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition
Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi +1
In-context learning (ICL) enables large language models (LLMs) to perform new tasks using only a few demonstrations. However, in Named Entity Recognition (NER), existing ICL method…
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…
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…