Search Results for author:
Found 54 papers, 16 papers with code
Offset Unlearning for Large Language Models
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as copyrighted, harmful, and private content has led to ethical and legal concerns.
Training Small Multimodal Models to Bridge Biomedical Competency Gap: A Case Study in Radiology Imaging
Frontier models such as GPT-4V still have major competency gaps in multimodal capabilities for biomedical applications.
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries
Summarizing clinical text is crucial in health decision-support and clinical research.
T-Rex: Text-assisted Retrosynthesis Prediction
As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule.
Foundation Models for Biomedical Image Segmentation: A Survey
This transformative technology, originally developed for general-purpose computer vision, has found rapid application in medical image processing.
When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology
This technical report presents LongViT, a vision Transformer that can process gigapixel images in an end-to-end manner.
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
Ranked #1 on
Question Answering
on MedQA
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text Generation
Medical text generation aims to assist with administrative work and highlight salient information to support decision-making.
TRIALSCOPE: A Unifying Causal Framework for Scaling Real-World Evidence Generation with Biomedical Language Models
The rapid digitization of real-world data offers an unprecedented opportunity for optimizing healthcare delivery and accelerating biomedical discovery.
Exploring the Boundaries of GPT-4 in Radiology
In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys
In a comprehensive battery of tests on counterfactual medical image generation, BiomedJourney substantially outperforms prior state-of-the-art methods in instruction image editing and medical image generation such as InstructPix2Pix and RoentGen.
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Instruction tuning has proven effective for distilling LLMs into more cost-efficient models such as Alpaca and Vicuna.
Ranked #1 on
Named Entity Recognition (NER)
on NCBI Disease
Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology
Clinical trial matching is a key process in health delivery and discovery.
Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events
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.
Automatic Calibration and Error Correction for Generative Large Language Models via Pareto Optimal Self-Supervision
Generative Large language models (LLMs) have demonstrated remarkable capabilities for a wide range of applications, but reducing ungrounded or erroneous responses remains a major growth area.
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
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.
Self-Verification Improves Few-Shot Clinical Information Extraction
Extracting patient information from unstructured text is a critical task in health decision-support and clinical research.
Compositional Zero-Shot Domain Transfer with Text-to-Text Models
We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning.
Context-faithful Prompting for Large Language Models
However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e. g., knowledge acquisition tasks).
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Therefore, training an effective generalist biomedical model requires high-quality multimodal data, such as parallel image-text pairs.
Ranked #3 on
Medical Visual Question Answering
on SLAKE-English
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction
Instead of obtaining features from language models, we propose BLIAM, a literature-based data synthesis approach to directly generate training data points that are interpretable and model-agnostic to downstream applications.
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction
In this paper, we aim at bridging the gap and propose to pretrain and finetune the RE model using consistent objectives of contrastive learning.
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain.
Ranked #1 on
Document Classification
on HOC
(Micro F1 metric)
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector representation space.
Ranked #1 on
Named Entity Recognition (NER)
on BC5CDR
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing.
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Results: We conduct an extensive study on 135, 107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states.
Fine-Tuning Large Neural Language Models for Biomedical Natural Language Processing
Overall, domainspecific vocabulary and pretraining facilitate more robust models for fine-tuning.
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
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.
Modular Self-Supervision for Document-Level Relation Extraction
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.
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
We first present deep probabilistic logic(DPL), which offers a unifying framework for task-specific self-supervision by composing probabilistic logic with deep learning.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
Targeted Adversarial Training for Natural Language Understanding
We present a simple yet effective Targeted Adversarial Training (TAT) algorithm to improve adversarial training for natural language understanding.
Self-supervised self-supervision by combining deep learning and probabilistic logic
Labeling training examples at scale is a perennial challenge in machine learning.
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 shown to improve many information retrieval benchmarks.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
Ranked #2 on
Participant Intervention Comparison Outcome Extraction
on EBM-NLP
(using extra training data)
Adversarial Training for Large Neural Language Models
In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning.
Ranked #6 on
Natural Language Inference
on ANLI test
(using extra training data)
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models.
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain.
Document-Level N-ary Relation Extraction with Multiscale Representation Learning
Most information extraction methods focus on binary relations expressed within single sentences.
Document-Level $N$-ary Relation Extraction with Multiscale Representation Learning
Widening the system's purview to the entire document maximizes potential recall.
Deep Probabilistic Logic: A Unifying Framework for Indirect Supervision
In this paper, we propose deep probabilistic logic (DPL) as a general framework for indirect supervision, by composing probabilistic logic with deep learning.
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation.
EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation
In science and other high-value domains, large repositories of data samples are often available, together with two sources of organic supervision: a lexicon for the annotation classes, and text descriptions that accompany some data samples.
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Past work in relation extraction has focused on binary relations in single sentences.
NLP for Precision Medicine
We will introduce precision medicine and showcase the vast opportunities for NLP in this burgeoning field with great societal impact.
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
We propose an efficient method to estimate the accuracy of classifiers using only unlabeled data.
Distant Supervision for Relation Extraction beyond the Sentence Boundary
At the core of our approach is a graph representation that can incorporate both standard dependencies and discourse relations, thus providing a unifying way to model relations within and across sentences.
Sum-Product Networks: A New Deep Architecture
Experiments show that inference and learning with SPNs can be both faster and more accurate than with standard deep networks.
