Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Trautmann, Dietrich"'
Autor:
Trautmann, Dietrich, Ostapuk, Natalia, Grail, Quentin, Pol, Adrian Alan, Bonifazi, Guglielmo, Gao, Shang, Gajek, Martin
In high-stakes domains like legal question-answering, the accuracy and trustworthiness of generative AI systems are of paramount importance. This work presents a comprehensive benchmark of various methods to assess the groundedness of AI-generated re
Externí odkaz:
http://arxiv.org/abs/2410.08764
Autor:
Trautmann, Dietrich
Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome. Chaining is a strategy used to decompose complex tasks into smaller, manageable components. In this study, we util
Externí odkaz:
http://arxiv.org/abs/2308.04138
Legal Prompt Engineering (LPE) or Legal Prompting is a process to guide and assist a large language model (LLM) with performing a natural legal language processing (NLLP) skill. Our goal is to use LPE with LLMs over long legal documents for the Legal
Externí odkaz:
http://arxiv.org/abs/2212.02199
Despite considerable recent progress, the creation of well-balanced and diverse resources remains a time-consuming and costly challenge in Argument Mining. Active Learning reduces the amount of data necessary for the training of machine learning mode
Externí odkaz:
http://arxiv.org/abs/2109.13611
Autor:
Trautmann, Dietrich
Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The t
Externí odkaz:
http://arxiv.org/abs/2011.00633
Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Conversational agents that can interact with users in a natural language
Externí odkaz:
http://arxiv.org/abs/2001.02284
The performance of a Part-of-speech (POS) tagger is highly dependent on the domain ofthe processed text, and for many domains there is no or only very little training data available. This work addresses the problem of POS tagging noisy user-generated
Externí odkaz:
http://arxiv.org/abs/1905.08920
Autor:
Trautmann, Dietrich, Daxenberger, Johannes, Stab, Christian, Schütze, Hinrich, Gurevych, Iryna
Prior work has commonly defined argument retrieval from heterogeneous document collections as a sentence-level classification task. Consequently, argument retrieval suffers both from low recall and from sentence segmentation errors making it difficul
Externí odkaz:
http://arxiv.org/abs/1904.09688
We take a practical approach to solving sequence labeling problem assuming unavailability of domain expertise and scarcity of informational and computational resources. To this end, we utilize a universal end-to-end Bi-LSTM-based neural sequence labe
Externí odkaz:
http://arxiv.org/abs/1808.03926
Publikováno v:
Datenbank-Spektrum; Jul2020, Vol. 20 Issue 2, p99-105, 7p
