7 papers
Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style
Marvin Limpijankit, Milad Alshomary, Yassin Oulad Daoud +9
VLMs have become increasingly proficient at a range of computer vision tasks, such as visual question answering and object detection. This includes increasingly strong capabilities…
iBERT: Interpretable Embeddings via Sense Decomposition
Vishal Anand, Milad Alshomary, Kathleen McKeown
We present iBERT (interpretable-BERT), an encoder to produce inherently interpretable and controllable embeddings - designed to modularize and expose the discriminative cues presen…
LLMs as Science Journalists: Supporting Early-stage Researchers in Communicating Their Science to the Public
Milad Alshomary, Grace Li, Anubhav Jangra +3
The scientific community needs tools that help early-stage researchers effectively communicate their findings and innovations to the public. Although existing general-purpose Large…
XAM: Interactive Explainability for Authorship Attribution Models
Milad Alshomary, Anisha Bhatnagar, Peter Zeng +3
We present IXAM, an Interactive eXplainability framework for Authorship Attribution Models. Given an authorship attribution (AA) task and an embedding-based AA model, our tool enab…
Layered Insights: Generalizable Analysis of Authorial Style by Leveraging All Transformer Layers
Milad Alshomary, Nikhil Reddy Varimalla, Vishal Anand +2
We propose a new approach for the authorship attribution task that leverages the various linguistic representations learned at different layers of pre-trained transformer-based mod…
Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution
Milad Alshomary, Narutatsu Ri, Marianna Apidianaki +3
Recent state-of-the-art authorship attribution methods learn authorship representations of texts in a latent, non-interpretable space, hindering their usability in real-world appli…