19 citations · 40 across the 21 of their papers we have counts for
25 papers
Review Rarely Beats the Stronger Model: Separating Verification from Capability Transfer in LLM Pipelines
Yirui Zhang, Yash Raj Shrestha
Qwen3-8B reviewing Llama-3.1-8B in math adds 33.2 points, yet Qwen3-8B alone is 6.4 points more accurate than the result. Reviewing another model's answer is usually judged by its…
Beyond Surface Cues: Disentangling Sociocultural Signals in Multilingual LLMs
Yuanjun Feng, Tanzhou Liu, Stefan Feuerriegel +1
Multilingual LLM outputs can vary across sociocultural contexts. However, evidence of cultural grounding can be misleading: identity labels may be inferred from explicit or indirec…
SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis
Niyoj Oli, Sachin Acharya, Sandesh Pokhrel +7
Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions and lifestyle habits. However, ea…
ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities
Aavash Chhetri, Bibek Niroula, Eduard Vazquez +4
In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or perform naive feature synthes…
Med-MMFL: A Multimodal Federated Learning Benchmark in Healthcare
Aavash Chhetri, Bibek Niroula, Pratik Shrestha +5
Federated learning (FL) enables collaborative model training across decentralized medical institutions while preserving data privacy. However, medical FL benchmarks remain scarce,…
Local K-Similarity Constraint for Federated Learning with Label Noise
Sanskar Amgain, Prashant Shrestha, Bidur Khanal +5
Federated learning on clients with noisy labels is a challenging problem, as such clients can infiltrate the global model, impacting the overall generalizability of the system. Exi…
Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making
Yuanjun Feng, Vivek Choudhary, Yash Raj Shrestha
Large language models (LLMs) are increasingly used in social science simulations. While their performance on reasoning and optimization tasks has been extensively evaluated, less a…
Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models
Bidur Khanal, Sandesh Pokhrel, Sanjay Bhandari +7
Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language. Existing VLMs demonstrate an i…
Human aversion? Do AI Agents Judge Identity More Harshly Than Performance
Yuanjun Feng, Vivek Chodhary, Yash Raj Shrestha
This study examines the understudied role of algorithmic evaluation of human judgment in hybrid decision-making systems, a critical gap in management research. While extant literat…
Surgical Vision World Model
Saurabh Koju, Saurav Bastola, Prashant Shrestha +4
Realistic and interactive surgical simulation has the potential to facilitate crucial applications, such as medical professional training and autonomous surgical agent training. In…
Contextualizing Recommendation Explanations with LLMs: A User Study
Yuanjun Feng, Stefan Feuerriegel, Yash Raj Shrestha
Large language models (LLMs) are increasingly prevalent in recommender systems, where LLMs can be used to generate personalized recommendations. Here, we examine how different LLM-…
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision
Sandesh Pokhrel, Sanjay Bhandari, Sharib Ali +7
The integration of deep learning tools in gastrointestinal vision holds the potential for significant advancements in diagnosis, treatment, and overall patient care. A major challe…
The Effect of Education in Prompt Engineering: Evidence from Journalists
Amirsiavosh Bashardoust, Yuanjun Feng, Dominique Geissler +2
Large language models (LLMs) are increasingly used in daily work. In this paper, we analyze whether training in prompt engineering can improve the interactions of users with LLMs.…
Difficulty Estimation and Simplification of French Text Using LLMs
Henri Jamet, Yash Raj Shrestha, Michalis Vlachos
We leverage generative large language models for language learning applications, focusing on estimating the difficulty of foreign language texts and simplifying them to lower diffi…
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities
Pranav Poudel, Prashant Shrestha, Sanskar Amgain +3
Multimodal AI has demonstrated superior performance over unimodal approaches by leveraging diverse data sources for more comprehensive analysis. However, applying this effectivenes…
Comparing the willingness to share for human-generated vs. AI-generated fake news
Amirsiavosh Bashardoust, Stefan Feuerriegel, Yash Raj Shrestha
Generative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that use…
ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis
Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez +2
Coronary Artery Diseases although preventable are one of the leading cause of mortality worldwide. Due to the onerous nature of diagnosis, tackling CADs has proved challenging. Thi…
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation
Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez +2
Coronary Artery Diseases (CADs) although preventable, are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive.…
Large Language Models for Difficulty Estimation of Foreign Language Content with Application to Language Learning
Michalis Vlachos, Mircea Lungu, Yash Raj Shrestha +1
We use large language models to aid learners enhance proficiency in a foreign language. This is accomplished by identifying content on topics that the user is interested in, and th…
Towards Automatic Bias Detection in Knowledge Graphs
Daphna Keidar, Mian Zhong, Ce Zhang +2
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous…
Augmenting Organizational Decision-Making with Deep Learning Algorithms: Principles, Promises, and Challenges
Yash Raj Shrestha, Vaibhav Krishna, Georg von Krogh
The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organiz…
A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records
Leopold Franz, Yash Raj Shrestha, Bibek Paudel
Augmentation of disease diagnosis and decision-making in healthcare with machine learning algorithms is gaining much impetus in recent years. In particular, in the current epidemio…
Adversarial Learning for Debiasing Knowledge Graph Embeddings
Mario Arduini, Lorenzo Noci, Federico Pirovano +3
Knowledge Graphs (KG) are gaining increasing attention in both academia and industry. Despite their diverse benefits, recent research have identified social and cultural biases emb…
How Credible is the Prediction of a Party-Based Election?
Jiong Guo, Yash Raj Shrestha, Yongjie Yang
In a party-based election system, the voters are grouped into parties and all voters of a party are assumed to vote according to the party preferences over the candidates. Hence, o…