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pro vyhledávání: '"Pesaranghader, Ali"'
Due to emergent capabilities, large language models (LLMs) have been utilized as language-based agents to perform a variety of tasks and make decisions with an increasing degree of autonomy. These autonomous agents can understand high-level instructi
Externí odkaz:
http://arxiv.org/abs/2408.11021
Large language models (LLMs) have brought autonomous agents closer to artificial general intelligence (AGI) due to their promising generalization and emergent capabilities. There is, however, a lack of studies on how LLM-based agents behave, why they
Externí odkaz:
http://arxiv.org/abs/2408.06318
Harmful and offensive communication or content is detrimental to social bonding and the mental state of users on social media platforms. Text detoxification is a crucial task in natural language processing (NLP), where the goal is removing profanity
Externí odkaz:
http://arxiv.org/abs/2404.03052
Autor:
Pesaranghader, Ali, Sajed, Touqir
Over the past two decades, recommendation systems (RSs) have used machine learning (ML) solutions to recommend items, e.g., movies, books, and restaurants, to clients of a business or an online platform. Recipe recommendation, however, has not yet re
Externí odkaz:
http://arxiv.org/abs/2308.04579
Autor:
Pour, Mohammad Mahdi Abdollah, Farinneya, Parsa, Toroghi, Armin, Korikov, Anton, Pesaranghader, Ali, Sajed, Touqir, Bharadwaj, Manasa, Mavrin, Borislav, Sanner, Scott
Publikováno v:
European Conference on Information Retrieval, pages 3--17, year 2023, Springer
As natural language interfaces enable users to express increasingly complex natural language queries, there is a parallel explosion of user review content that can allow users to better find items such as restaurants, books, or movies that match thes
Externí odkaz:
http://arxiv.org/abs/2308.00762
Autor:
Floto, Griffin, Pour, Mohammad Mahdi Abdollah, Farinneya, Parsa, Tang, Zhenwei, Pesaranghader, Ali, Bharadwaj, Manasa, Sanner, Scott
Text detoxification is a conditional text generation task aiming to remove offensive content from toxic text. It is highly useful for online forums and social media, where offensive content is frequently encountered. Intuitively, there are diverse wa
Externí odkaz:
http://arxiv.org/abs/2306.08505
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate am
Externí odkaz:
http://arxiv.org/abs/1802.09059
Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically evolves over tim
Externí odkaz:
http://arxiv.org/abs/1710.02030
The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In
Externí odkaz:
http://arxiv.org/abs/1709.02457
Autor:
Pesaranghader, Ali
Continuous change and development are essential aspects of evolving environments and applications, including, but not limited to, smart cities, military, medicine, nuclear reactors, self-driving cars, aviation, and aerospace. That is, the fundamental
Externí odkaz:
http://hdl.handle.net/10393/38190