22 citations · 33 across the 17 of their papers we have counts for
18 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…
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…
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…
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…
"Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline
Grace Li, Milad Alshomary, Smaranda Muresan
Explanations form the foundation of knowledge sharing and build upon communication principles, social dynamics, and learning theories. We focus specifically on conversational appro…
Modeling the Quality of Dialogical Explanations
Milad Alshomary, Felix Lange, Meisam Booshehri +3
Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an {\em explainer} discusses a concept or phenomenon of interest with an {\em explainee}. Leavi…
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments
Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary +11
We present the Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we…
Conclusion-based Counter-Argument Generation
Milad Alshomary, Henning Wachsmuth
In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural…
"Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations
Henning Wachsmuth, Milad Alshomary
As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise…
The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments
Milad Alshomary, Roxanne El Baff, Timon Gurcke +1
An audience's prior beliefs and morals are strong indicators of how likely they will be affected by a given argument. Utilizing such knowledge can help focus on shared values to br…
Assessing the Sufficiency of Arguments through Conclusion Generation
Timon Gurcke, Milad Alshomary, Henning Wachsmuth
The premises of an argument give evidence or other reasons to support a conclusion. However, the amount of support required depends on the generality of a conclusion, the nature of…
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
Milad Alshomary, Timon Gurcke, Shahbaz Syed +5
Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper p…
Generating Informative Conclusions for Argumentative Texts
Shahbaz Syed, Khalid Al-Khatib, Milad Alshomary +2
The purpose of an argumentative text is to support a certain conclusion. Yet, they are often omitted, expecting readers to infer them rather. While appropriate when reading an indi…
Argument Undermining: Counter-Argument Generation by Attacking Weak Premises
Milad Alshomary, Shahbaz Syed, Arkajit Dhar +2
Text generation has received a lot of attention in computational argumentation research as of recent. A particularly challenging task is the generation of counter-arguments. So far…
Belief-based Generation of Argumentative Claims
Milad Alshomary, Wei-Fan Chen, Timon Gurcke +1
When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computationa…
Wikipedia Text Reuse: Within and Without
Milad Alshomary, Michael Völske, Tristan Licht +4
We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample o…