21 citations · 71 across the 31 of their papers we have counts for
17 papers · 1 filter
Question-Specific Knowledge Graphs for Efficient Visual Reasoning
Ting-Chih Chen, Emile van Krieken, Shujian Yu +1
Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs into textual representations.…
M-QUEST -- Meme Question-Understanding Evaluation on Semantics and Toxicity
Stefano De Giorgis, Ting-Chih Chen, Filip Ilievski
Internet memes are a powerful form of online communication, yet their nature and reliance on commonsense knowledge make toxicity detection challenging. Identifying key features for…
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
Yifan Jiang, Filip Ilievski, Kaixin Ma +1
The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms. While su…
Robust Text Classification: Analyzing Prototype-Based Networks
Zhivar Sourati, Darshan Deshpande, Filip Ilievski +2
Downstream applications often require text classification models to be accurate and robust. While the accuracy of the state-of-the-art Language Models (LMs) approximates human perf…
ARN: Analogical Reasoning on Narratives
Zhivar Sourati, Filip Ilievski, Pia Sommerauer +1
As a core cognitive skill that enables the transferability of information across domains, analogical reasoning has been extensively studied for both humans and computational models…
A Study of Situational Reasoning for Traffic Understanding
Jiarui Zhang, Filip Ilievski, Kaixin Ma +3
Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure. Understanding traffic situation…
Contextualizing Argument Quality Assessment with Relevant Knowledge
Darshan Deshpande, Zhivar Sourati, Filip Ilievski +1
Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real-world ar…
Knowledge-enhanced Agents for Interactive Text Games
Prateek Chhikara, Jiarui Zhang, Filip Ilievski +2
Communication via natural language is a key aspect of machine intelligence, and it requires computational models to learn and reason about world concepts, with varying levels of su…
Transferring Procedural Knowledge across Commonsense Tasks
Yifan Jiang, Filip Ilievski, Kaixin Ma
Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the lon…
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Jiarui Zhang, Filip Ilievski, Aravinda Kollaa +3
Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge. Prior work has provided sufficient perce…
Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments
Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande +4
The spread of misinformation, propaganda, and flawed argumentation has been amplified in the Internet era. Given the volume of data and the subtlety of identifying violations of ar…
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Peifeng Wang, Aaron Chan, Filip Ilievski +2
Neural language models (LMs) have achieved impressive results on various language-based reasoning tasks by utilizing latent knowledge encoded in their own pretrained parameters. To…
Coalescing Global and Local Information for Procedural Text Understanding
Kaixin Ma, Filip Ilievski, Jonathan Francis +2
Procedural text understanding is a challenging language reasoning task that requires models to track entity states across the development of a narrative. A complete procedural unde…
An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs
Jiarui Zhang, Filip Ilievski, Kaixin Ma +2
Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models, in zero-shot evaluation on various…
Generalizable Neuro-symbolic Systems for Commonsense Question Answering
Alessandro Oltramari, Jonathan Francis, Filip Ilievski +2
This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for…
Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models
Kaixin Ma, Filip Ilievski, Jonathan Francis +3
Commonsense reasoning benchmarks have been largely solved by fine-tuning language models. The downside is that fine-tuning may cause models to overfit to task-specific data and the…
PaCo: Preconditions Attributed to Commonsense Knowledge
Ehsan Qasemi, Filip Ilievski, Muhao Chen +1
Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the wa…