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Found 15 papers, 9 papers with code
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation
Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong information.
AIPatient: Simulating Patients with EHRs and LLM Powered Agentic Workflow
Here, we developed AIPatient, an advanced simulated patient system with AIPatient Knowledge Graph (AIPatient KG) as the input and the Reasoning Retrieval-Augmented Generation (Reasoning RAG) agentic workflow as the generation backbone.
Dense Point Clouds Matter: Dust-GS for Scene Reconstruction from Sparse Viewpoints
3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in scene synthesis and novel view synthesis tasks.
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering.
Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks
Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases.
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data.
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Generative models have been showing potential for producing data in mass.
The impact of responding to patient messages with large language model assistance
Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously.
Measuring Pointwise $\mathcal{V}$-Usable Information In-Context-ly
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.
Large Language Models to Identify Social Determinants of Health in Electronic Health Records
The study also experimented with synthetic data generation and assessed for algorithmic bias.
Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification
The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice.
Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy
We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT.
BCH-NLP at BioCreative VII Track 3: medications detection in tweets using transformer networks and multi-task learning
In this paper, we present our work participating in the BioCreative VII Track 3 - automatic extraction of medication names in tweets, where we implemented a multi-task learning model that is jointly trained on text classification and sequence labelling.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Thinkey: A Scalable Blockchain Architecture
This paper presents Thinkey, an efficient, secure, infinitely scalable and decentralized blockchain architecture.
Cryptography and Security
