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Found 3 papers, 1 papers with code
WangLab at MEDIQA-M3G 2024: Multimodal Medical Answer Generation using Large Language Models
This paper outlines our submission to the MEDIQA2024 Multilingual and Multimodal Medical Answer Generation (M3G) shared task.
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval
Via extensive automatic and human evaluation, we determine: (1) state-of-the-art summarizers suffer large reductions in performance when applied to open-domain MDS, (2) additional training in the open-domain setting can reduce this sensitivity to imperfect retrieval, and (3) summarizers are insensitive to the retrieval of duplicate documents and the order of retrieved documents, but highly sensitive to other errors, like the retrieval of irrelevant documents.
Ranked #1 on
Multi-Document Summarization
on MS^2
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
Inspired by recent advances in deep metric learning (DML), we carefully design a self-supervised objective for learning universal sentence embeddings that does not require labelled training data.
Ranked #4 on
Linear-Probe Classification
on SentEval
