69 citations · 175 across the 40 of their papers we have counts for
5 papers · 1 filter
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
Fangxin Wang, Peyman Baghershahi, Langzhou He +3
Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for of…
Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
Md Aminul Islam, Ahmed Sayeed Faruk, Sourav Medya +1
Collaborative filtering (CF) models based on graph neural networks (GNNs) achieve strong performance in recommender systems by propagating user-item signals over interaction graphs…
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
Peyman Baghershahi, Fangxin Wang, Debmalya Mandal +1
Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) re…
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models
Angelo Zangari, Peyman Baghershahi, Sourav Medya
Graph problems are fundamentally challenging for large language models (LLMs). While LLMs excel at processing unstructured text, graph tasks require reasoning over explicit structu…
Helping the Helper: LLM-Assisted Problem Articulation for Older Adults Seeking Technology Support
Hasti Sharifi, Homaira Huda Shomee, Melissa Lamar +2
Older adults often struggle to articulate technology support needs due to unfamiliar technical terminology and age-related cognitive changes. We explore how large language models (…