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Found 8 papers, 1 papers with code
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic
While significant progress has been made in benchmarking Large Language Models (LLMs) across various tasks, there is a lack of comprehensive evaluation of their abilities in responding to multi-turn instructions in less-commonly tested languages like Arabic.
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language.
LAraBench: Benchmarking Arabic AI with Large Language Models
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department
In the experimental analysis, we show that mutli-modality improves the prediction performance compared with models trained solely on text or vital signs.
Learning a Shared Model for Motorized Prosthetic Joints to Predict Ankle-Joint Motion
Control strategies for active prostheses or orthoses use sensor inputs to recognize the user's locomotive intention and generate corresponding control commands for producing the desired locomotion.
Fairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show
In this wo rk, we are interested in developing deep learning models for no-show prediction based on tabular data while ensuring fairness properties.
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR.
Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels
The network is alternately trained on epochs with the clean dataset with a simple cross-entropy loss and on next epoch with the noisy dataset and a loss corrected with the estimated corruption matrix.
