Search Results for author: Cliff Wong

Found 13 papers, 1 papers with code

Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events

no code implementations12 Jul 2023 Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, Hoifung Poon

We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access.

Self-Supervised Learning

LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

no code implementations NeurIPS 2023 Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao

In this paper, we propose a cost-efficient approach for training a vision-language conversational assistant that can answer open-ended research questions of biomedical images.

Instruction Following Language Modelling +2

Knowledge-Rich Self-Supervision for Biomedical Entity Linking

no code implementations15 Dec 2021 Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia.

Contrastive Learning Entity Linking

Modular Self-Supervision for Document-Level Relation Extraction

no code implementations EMNLP 2021 Sheng Zhang, Cliff Wong, Naoto Usuyama, Sarthak Jain, Tristan Naumann, Hoifung Poon

Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.

Document-level Relation Extraction Reading Comprehension +1

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