Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Glavas, Goran"'
Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders
Encoder architectures play a pivotal role in neural news recommenders by embedding the semantic and contextual information of news and users. Thus, research has heavily focused on enhancing the representational capabilities of news and user encoders
Externí odkaz:
http://arxiv.org/abs/2410.01470
Autor:
Keller, Lennart, Glavaš, Goran
Recent advancements in multilingual speech encoding as well as transcription raise the question of the most effective approach to semantic speech classification. Concretely, can (1) end-to-end (E2E) classifiers obtained by fine-tuning state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2409.06372
Autor:
Dutta, Subhabrata, Kaufmann, Timo, Glavaš, Goran, Habernal, Ivan, Kersting, Kristian, Kreuter, Frauke, Mezini, Mira, Gurevych, Iryna, Hüllermeier, Eyke, Schuetze, Hinrich
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the
Externí odkaz:
http://arxiv.org/abs/2408.07461
Multilingual sentence encoders are commonly obtained by training multilingual language models to map sentences from different languages into a shared semantic space. As such, they are subject to curse of multilinguality, a loss of monolingual represe
Externí odkaz:
http://arxiv.org/abs/2407.14878
Research on token-level reference-free hallucination detection has predominantly focused on English, primarily due to the scarcity of robust datasets in other languages. This has hindered systematic investigations into the effectiveness of cross-ling
Externí odkaz:
http://arxiv.org/abs/2407.13702
The process mining community has recently recognized the potential of large language models (LLMs) for tackling various process mining tasks. Initial studies report the capability of LLMs to support process analysis and even, to some extent, that the
Externí odkaz:
http://arxiv.org/abs/2407.02310
Recent Large Vision-Language Models (LVLMs) demonstrate impressive abilities on numerous image understanding and reasoning tasks. The task of fine-grained object classification (e.g., distinction between \textit{animal species}), however, has been pr
Externí odkaz:
http://arxiv.org/abs/2406.14496
Large vision-language models (LVLMs) have recently dramatically pushed the state of the art in image captioning and many image understanding tasks (e.g., visual question answering). LVLMs, however, often \textit{hallucinate} and produce captions that
Externí odkaz:
http://arxiv.org/abs/2406.14492
LLMs have become a go-to solution not just for text generation, but also for natural language understanding (NLU) tasks. Acquiring extensive knowledge through language modeling on web-scale corpora, they excel on English NLU, yet struggle to extend t
Externí odkaz:
http://arxiv.org/abs/2406.12739
Rapidly growing numbers of multilingual news consumers pose an increasing challenge to news recommender systems in terms of providing customized recommendations. First, existing neural news recommenders, even when powered by multilingual language mod
Externí odkaz:
http://arxiv.org/abs/2406.12634