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Found 24 papers, 13 papers with code
AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception
To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models.
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
The advancement of natural language processing (NLP) systems in healthcare hinges on language model ability to interpret the intricate information contained within clinical notes.
JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning and Professional Question Answering Capability
Unlike previous methods in RAG where the retrieval model was trained separately from the LLM, we introduce JMLR (for Jointly trains LLM and information Retrieval (IR)) during the fine-tuning phase.
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
An obvious obstacle lies in the absence of a specific benchmark to evaluate the effectiveness of MLLMs on aesthetic perception.
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP
The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR).
Surpassing GPT-4 Medical Coding with a Two-Stage Approach
Recent advances in large language models (LLMs) show potential for clinical applications, such as clinical decision support and trial recommendations.
Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation
We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.
SELF-EXPLAIN: Teaching Large Language Models to Reason Complex Questions by Themselves
Large language models (LLMs) can generate intermediate reasoning steps.
BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing
We also examined whether categories(e. g., QA, IE, and generation) of instructions impact model performance.
NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes
We introduce NoteChat, a novel cooperative multi-agent framework leveraging Large Language Models (LLMs) to generate patient-physician dialogues.
UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets.
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
We propose a step-by-step planning approach for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps.
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information
Specifically, we suggest employing Bayesian inference to incorporate the sense definitions when sense information of the answer is not provided.
Copula-based transferable models for synthetic population generation
Population synthesis involves generating synthetic yet realistic representations of a target population of micro-agents for behavioral modeling and simulation.
Enhancing the prediction of disease outcomes using electronic health records and pretrained deep learning models
Question: Can an encoder-decoder architecture pretrained on a large dataset of longitudinal electronic health records improves patient outcome predictions?
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide.
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning
The transfer learning framework helps SOAP classification model's inter-hospital migration with a minimal size of the manually annotated dataset.
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
This task is challenging due to the high-dimensional space of multi-label assignment (155, 000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically.
Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing
Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time.
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3, 000+ tokens.
Ranked #1 on
Medical Code Prediction
on MIMIC-III
Extracting Biomedical Factual Knowledge Using Pretrained Language Model and Electronic Health Record Context
In order to make LMs as KBs more in line with the actual application scenarios of the biomedical domain, we specifically add EHR notes as context to the prompt to improve the low bound in the biomedical domain.
Generating Accurate Electronic Health Assessment from Medical Graph
One of the fundamental goals of artificial intelligence is to build computer-based expert systems.
Generating Classical Chinese Poems from Vernacular Chinese
According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models.
Cultural Vocal Bursts Intensity Prediction
Reinforcement Learning (RL)
+2
Word Embedding Perturbation for Sentence Classification
In this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods.
