Zobrazeno 1 - 10
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pro vyhledávání: '"Heumann, Christian"'
Autor:
Arias, Esteban Garces, Blocher, Hannah, Rodemann, Julian, Li, Meimingwei, Heumann, Christian, Aßenmacher, Matthias
Open-ended text generation has become a prominent task in natural language processing due to the rise of powerful (large) language models. However, evaluating the quality of these models and the employed decoding strategies remains challenging becaus
Externí odkaz:
http://arxiv.org/abs/2410.18653
Decoding strategies for large language models (LLMs) are a critical but often underexplored aspect of text generation tasks. Since LLMs produce probability distributions over the entire vocabulary, various decoding methods have been developed to tran
Externí odkaz:
http://arxiv.org/abs/2410.06097
In recent years, large language models (LLMs) have emerged as powerful tools with potential applications in various fields, including software engineering. Within the scope of this research, we evaluate five different state-of-the-art LLMs - Bard, Bi
Externí odkaz:
http://arxiv.org/abs/2409.04164
Autor:
Arias, Esteban Garces, Rodemann, Julian, Li, Meimingwei, Heumann, Christian, Aßenmacher, Matthias
Decoding from the output distributions of large language models to produce high-quality text is a complex challenge in language modeling. Various approaches, such as beam search, sampling with temperature, $k-$sampling, nucleus $p-$sampling, typical
Externí odkaz:
http://arxiv.org/abs/2407.18698
Autor:
Obster, Fabian, Heumann, Christian
This paper introduces the sgboost package in R, which implements sparse-group boosting for modeling high-dimensional data with natural groupings in covariates. Sparse-group boosting offers a flexible approach for both group and individual variable se
Externí odkaz:
http://arxiv.org/abs/2405.21037
Autor:
Pal, Samyajoy, Heumann, Christian
This study tackles the efficient estimation of Kullback-Leibler (KL) Divergence in Dirichlet Mixture Models (DMM), crucial for clustering compositional data. Despite the significance of DMMs, obtaining an analytically tractable solution for KL Diverg
Externí odkaz:
http://arxiv.org/abs/2403.12158
Autor:
Scholbeck, Christian A., Moosbauer, Julia, Casalicchio, Giuseppe, Gupta, Hoshin, Bischl, Bernd, Heumann, Christian
We argue that interpretations of machine learning (ML) models or the model-building process can be seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engin
Externí odkaz:
http://arxiv.org/abs/2312.13234
Autor:
Löwe, Holger, Scholbeck, Christian A., Heumann, Christian, Bischl, Bernd, Casalicchio, Giuseppe
Forward marginal effects have recently been introduced as a versatile and effective model-agnostic interpretation method particularly suited for non-linear and non-parametric prediction models. They provide comprehensible model explanations of the fo
Externí odkaz:
http://arxiv.org/abs/2310.02008
Autor:
Urchs, Stefanie, Thurner, Veronika, Aßenmacher, Matthias, Heumann, Christian, Thiemichen, Stephanie
With the introduction of ChatGPT, OpenAI made large language models (LLM) accessible to users with limited IT expertise. However, users with no background in natural language processing (NLP) might lack a proper understanding of LLMs. Thus the awaren
Externí odkaz:
http://arxiv.org/abs/2310.03031
Autor:
Koch, Philipp, Nuñez, Gilary Vera, Arias, Esteban Garces, Heumann, Christian, Schöffel, Matthias, Häberlin, Alexander, Aßenmacher, Matthias
The Bavarian Academy of Sciences and Humanities aims to digitize its Medieval Latin Dictionary. This dictionary entails record cards referring to lemmas in medieval Latin, a low-resource language. A crucial step of the digitization process is the Han
Externí odkaz:
http://arxiv.org/abs/2308.09368