Zobrazeno 1 - 10
of 5 363
pro vyhledávání: '"A. Potthast"'
Autor:
Potthast, Nicolas
We determine the distribution of discriminants of wildly ramified elementary-abelian extensions of local and global function fields in characteristic $p$. For local and rational function fields, we also give precise formulae for the number of element
Externí odkaz:
http://arxiv.org/abs/2408.16394
Evaluating the output of generative large language models (LLMs) is challenging and difficult to scale. Most evaluations of LLMs focus on tasks such as single-choice question-answering or text classification. These tasks are not suitable for assessin
Externí odkaz:
http://arxiv.org/abs/2408.09831
Publikováno v:
Heritage Science, Vol 8, Iss 1, Pp 1-15 (2020)
Abstract Because of its acidic and oxidative nature, iron gall ink promotes the endogenous degradation of paper manuscripts. Mechanical damage in areas of concentrated ink application or along mechanically stressed edges or folds results in problems
Externí odkaz:
https://doaj.org/article/fd4053e2064b478883c257ceb07d51af
Representation-based retrieval models, so-called biencoders, estimate the relevance of a document to a query by calculating the similarity of their respective embeddings. Current state-of-the-art biencoders are trained using an expensive training reg
Externí odkaz:
http://arxiv.org/abs/2407.21515
Autor:
Thakur, Nandan, Bonifacio, Luiz, Fröbe, Maik, Bondarenko, Alexander, Kamalloo, Ehsan, Potthast, Martin, Hagen, Matthias, Lin, Jimmy
The zero-shot effectiveness of neural retrieval models is often evaluated on the BEIR benchmark -- a combination of different IR evaluation datasets. Interestingly, previous studies found that particularly on the BEIR subset Touch\'e 2020, an argumen
Externí odkaz:
http://arxiv.org/abs/2407.07790
Autor:
Keller, Jan D., Potthast, Roland
The integration of observational data into numerical models, known as data assimilation (DA), is fundamental for making Numerical Weather Prediction (NWP) possible, with breathtaking success over the past 60 years (Bauer et al. 2015). Traditional DA
Externí odkaz:
http://arxiv.org/abs/2406.00390
Autor:
Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin, Hagen, Matthias
Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, the distilled models usually do not reach their teacher LLM's effectiveness. To investiga
Externí odkaz:
http://arxiv.org/abs/2405.07920
Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers. Since warnings about a document intuitively need to be shown before reading it, authors usually assi
Externí odkaz:
http://arxiv.org/abs/2404.09615
Autor:
Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin, Hagen, Matthias
Existing cross-encoder re-rankers can be categorized as pointwise, pairwise, or listwise models. Pair- and listwise models allow passage interactions, which usually makes them more effective than pointwise models but also less efficient and less robu
Externí odkaz:
http://arxiv.org/abs/2404.06912
Since paraphrasing is an ill-defined task, the term "paraphrasing" covers text transformation tasks with different characteristics. Consequently, existing paraphrasing studies have applied quite different (explicit and implicit) criteria as to when a
Externí odkaz:
http://arxiv.org/abs/2403.17564