Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Aßenmacher Matthias"'
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
Autor:
Wuttke, Alexander, Aßenmacher, Matthias, Klamm, Christopher, Lang, Max M., Würschinger, Quirin, Kreuter, Frauke
Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability to express unanticipated thoughts in their own words, while conversatio
Externí odkaz:
http://arxiv.org/abs/2410.01824
To reduce the need for human annotations, large language models (LLMs) have been proposed as judges of the quality of other candidate models. LLM judges are typically evaluated by measuring the correlation with human judgments on generation tasks suc
Externí odkaz:
http://arxiv.org/abs/2409.04168
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:
Deiseroth, Björn, Meuer, Max, Gritsch, Nikolas, Eichenberg, Constantin, Schramowski, Patrick, Aßenmacher, Matthias, Kersting, Kristian
Large Language Models (LLMs) have reshaped natural language processing with their impressive capabilities. However, their ever-increasing size has raised concerns about their effective deployment and the need for LLM compression. This study introduce
Externí odkaz:
http://arxiv.org/abs/2311.01544
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
Annotating costs of large corpora are still one of the main bottlenecks in empirical social science research. On the one hand, making use of the capabilities of domain transfer allows re-using annotated data sets and trained models. On the other hand
Externí odkaz:
http://arxiv.org/abs/2307.16511