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pro vyhledávání: '"Ilievski, Filip"'
Autor:
Allen, Bradley P., Ilievski, Filip
Publikováno v:
Transactions on Graph Data and Knowledge, Vol 2, Iss 1, Pp 5:1-5:23 (2024)
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for
Externí odkaz:
https://doaj.org/article/0ccc1392817c4b52bcea6b2ddcd89dd0
While visual question-answering (VQA) benchmarks have catalyzed the development of reasoning techniques, they have focused on vertical thinking. Effective problem-solving also necessitates lateral thinking, which remains understudied in AI and has no
Externí odkaz:
http://arxiv.org/abs/2409.04053
While vertical thinking relies on logical and commonsense reasoning, lateral thinking requires systems to defy commonsense associations and overwrite them through unconventional thinking. Lateral thinking has been shown to be challenging for current
Externí odkaz:
http://arxiv.org/abs/2404.16068
Autor:
Jiang, Yifan, Zhang, Jiarui, Sun, Kexuan, Sourati, Zhivar, Ahrabian, Kian, Ma, Kaixin, Ilievski, Filip, Pujara, Jay
While multi-modal large language models (MLLMs) have shown significant progress on many popular visual reasoning benchmarks, whether they possess abstract visual reasoning abilities remains an open question. Similar to the Sudoku puzzles, abstract vi
Externí odkaz:
http://arxiv.org/abs/2404.13591
According to WWF, 1.1 billion people lack access to water, and 2.7 billion experience water scarcity at least one month a year. By 2025, two-thirds of the world's population may be facing water shortages. This highlights the urgency of managing water
Externí odkaz:
http://arxiv.org/abs/2403.17426
Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perception. In particular, while prior works have provided anecdotal evide
Externí odkaz:
http://arxiv.org/abs/2402.07384
Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. Whil
Externí odkaz:
http://arxiv.org/abs/2311.11157
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 performance, they often exhibit a drop in performance on noisy data found
Externí odkaz:
http://arxiv.org/abs/2311.06647
Multimodal Large Language Models (MLLMs) have recently achieved promising zero-shot accuracy on visual question answering (VQA) -- a fundamental task affecting various downstream applications and domains. Given the great potential for the broad use o
Externí odkaz:
http://arxiv.org/abs/2310.16033
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 such vertical thinking tasks have been relatively popular, lateral think
Externí odkaz:
http://arxiv.org/abs/2310.05057