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08/07/2023
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Large language models (LLMs) have demonstrated remarkable generalizabili...
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08/04/2023
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Clinical trial matching is a key process in health delivery and discover...
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07/12/2023
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
Large language models (LLMs), such as GPT-4, have demonstrated remarkabl...
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06/28/2023
LLM Calibration and Automatic Hallucination Detection via Pareto Optimal Self-supervision
Large language models (LLMs) have demonstrated remarkable capabilities o...
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06/01/2023
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Conversational generative AI has demonstrated remarkable promise for emp...
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03/20/2023
Context-faithful Prompting for Large Language Models
Large language models (LLMs) encode parametric knowledge about world fac...
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03/02/2023
Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing
Contrastive pretraining on parallel image-text data has attained great s...
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02/14/2023
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction
Language models pre-trained on scientific literature corpora have substa...
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12/21/2022
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction
Relation extraction (RE), which has relied on structurally annotated cor...
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10/19/2022
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
Pre-trained language models have attracted increasing attention in the b...
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08/30/2022
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
We present an efficient bi-encoder framework for named entity recognitio...
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04/21/2022
Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing
Multi-modal data abounds in biomedicine, such as radiology images and re...
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03/20/2022
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Objective: The majority of detailed patient information in real-world da...
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12/15/2021
Knowledge-Rich Self-Supervised Entity Linking
Entity linking faces significant challenges, such as prolific variations...
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12/15/2021
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Motivation: A perennial challenge for biomedical researchers and clinica...
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09/11/2021
Modular Self-Supervision for Document-Level Relation Extraction
Extracting relations across large text spans has been relatively underex...
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07/27/2021
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
Deep learning has proven effective for various application tasks, but it...
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06/25/2021
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
Information overload is a prevalent challenge in many high-value domains...
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04/12/2021
Targeted Adversarial Training for Natural Language Understanding
We present a simple yet effective Targeted Adversarial Training (TAT) al...
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12/23/2020
Self-supervised self-supervision by combining deep learning and probabilistic logic
Labeling training examples at scale is a perennial challenge in machine ...
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11/03/2020
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Neural rankers based on deep pretrained language models (LMs) have been ...
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07/31/2020
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Pretraining large neural language models, such as BERT, has led to impre...
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04/20/2020
Adversarial Training for Large Neural Language Models
Generalization and robustness are both key desiderata for designing mach...
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02/19/2020
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
We present MT-DNN, an open-source natural language understanding (NLU) t...
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06/11/2019
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
This paper describes our competing system to enter the MEDIQA-2019 compe...
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04/04/2019
Document-Level N-ary Relation Extraction with Multiscale Representation Learning
Most information extraction methods focus on binary relations expressed ...
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08/26/2018
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision
Deep learning has emerged as a versatile tool for a wide range of NLP ta...
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11/10/2017
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
The study and understanding of human behaviour is relevant to computer s...
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09/25/2017
EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation
Many real-world applications require large-scale data annotation, such a...
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08/12/2017
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Past work in relation extraction has focused on binary relations in sing...
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05/19/2017
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
We propose an efficient method to estimate the accuracy of classifiers u...
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02/14/2012