Zobrazeno 1 - 10
of 9 450
pro vyhledávání: '"Staab, A."'
Retrieval-Augmented Generation (RAG) improves LLMs by enabling them to incorporate external data during generation. This raises concerns for data owners regarding unauthorized use of their content in RAG systems. Despite its importance, the challenge
Externí odkaz:
http://arxiv.org/abs/2410.03537
LLM watermarks stand out as a promising way to attribute ownership of LLM-generated text. One threat to watermark credibility comes from spoofing attacks, where an unauthorized third party forges the watermark, enabling it to falsely attribute arbitr
Externí odkaz:
http://arxiv.org/abs/2410.02693
In autonomous driving, High Definition (HD) maps provide a complete lane model that is not limited by sensor range and occlusions. However, the generation and upkeep of HD maps involves periodic data collection and human annotations, limiting scalabi
Externí odkaz:
http://arxiv.org/abs/2409.12409
Attention mechanisms and Transformer architectures have revolutionized Natural Language Processing (NLP) by enabling exceptional modeling of long-range dependencies and capturing intricate linguistic patterns. However, their inherent reliance on line
Externí odkaz:
http://arxiv.org/abs/2409.12175
Autor:
Zhu, Yuqicheng, Potyka, Nico, Pan, Jiarong, Xiong, Bo, He, Yunjie, Kharlamov, Evgeny, Staab, Steffen
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The learned KG embeddings are typically used to answer queries by ranking a
Externí odkaz:
http://arxiv.org/abs/2408.08248
Autor:
Zhu, Yuqicheng, Potyka, Nico, Nayyeri, Mojtaba, Xiong, Bo, He, Yunjie, Kharlamov, Evgeny, Staab, Steffen
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link prediction yet give conflicting predictions for unseen queries. This p
Externí odkaz:
http://arxiv.org/abs/2408.08226
Over the past few years, we have seen the emergence of large knowledge graphs combining information from multiple sources. Sometimes, this information is provided in the form of assertions about other assertions, defining contexts where assertions ar
Externí odkaz:
http://arxiv.org/abs/2407.21483
Autor:
Zhu, Yuqicheng, Potyka, Nico, Xiong, Bo, Tran, Trung-Kien, Nayyeri, Mojtaba, Kharlamov, Evgeny, Staab, Steffen
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilistic inference efficiently using the example of Statistical EL (SEL), a s
Externí odkaz:
http://arxiv.org/abs/2407.11821
Autor:
He, Yunjie, Hernandez, Daniel, Nayyeri, Mojtaba, Xiong, Bo, Zhu, Yuqicheng, Kharlamov, Evgeny, Staab, Steffen
Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily
Externí odkaz:
http://arxiv.org/abs/2407.09212
Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users worldwide. However, their strong capabilities and vast world knowledge do not come without associated privacy risks. In this work, we focus
Externí odkaz:
http://arxiv.org/abs/2406.07217