Zobrazeno 1 - 10
of 199
pro vyhledávání: '"Vossen, Piek"'
We develop an artificial agent motivated to augment its knowledge base beyond its initial training. The agent actively participates in dialogues with other agents, strategically acquiring new information. The agent models its knowledge as an RDF know
Externí odkaz:
http://arxiv.org/abs/2406.19500
Social media conversations frequently suffer from toxicity, creating significant issues for users, moderators, and entire communities. Events in the real world, like elections or conflicts, can initiate and escalate toxic behavior online. Our study i
Externí odkaz:
http://arxiv.org/abs/2405.13754
Recent work has demonstrated that the latent spaces of large language models (LLMs) contain directions predictive of the truth of sentences. Multiple methods recover such directions and build probes that are described as getting at a model's "knowled
Externí odkaz:
http://arxiv.org/abs/2404.18865
Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often overlooked aspect in this domain: the influence of the source language of a language model on language transfer perform
Externí odkaz:
http://arxiv.org/abs/2404.18810
Toxic language remains an ongoing challenge on social media platforms, presenting significant issues for users and communities. This paper provides a cross-topic and cross-lingual analysis of toxicity in Reddit conversations. We collect 1.5 million c
Externí odkaz:
http://arxiv.org/abs/2404.18726
Autor:
van der Meer, Michiel, Liscio, Enrico, Jonker, Catholijn M., Plaat, Aske, Vossen, Piek, Murukannaiah, Pradeep K.
Publikováno v:
Journal of Artificial Intelligence Research (JAIR), 80:1187-1222, 2024
Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract
Externí odkaz:
http://arxiv.org/abs/2403.09713
Presenting high-level arguments is a crucial task for fostering participation in online societal discussions. Current argument summarization approaches miss an important facet of this task -- capturing diversity -- which is important for accommodatin
Externí odkaz:
http://arxiv.org/abs/2402.01535
Disagreements are common in online discussions. Disagreement may foster collaboration and improve the quality of a discussion under some conditions. Although there exist methods for recognizing disagreement, a deeper understanding of factors that inf
Externí odkaz:
http://arxiv.org/abs/2310.15757
Natural language reasoning plays an increasingly important role in improving language models' ability to solve complex language understanding tasks. An interesting use case for reasoning is the resolution of context-dependent ambiguity. But no resour
Externí odkaz:
http://arxiv.org/abs/2310.14657
Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift. To explore this phenomenon, we evaluate three approaches for detecting toxic spans under cross-domain conditions
Externí odkaz:
http://arxiv.org/abs/2306.09642