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pro vyhledávání: '"A. Matthes"'
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many Classes
Scientific document classification is a critical task and often involves many classes. However, collecting human-labeled data for many classes is expensive and usually leads to label-scarce scenarios. Moreover, recent work has shown that sentence emb
Externí odkaz:
http://arxiv.org/abs/2410.05770
The field of privacy-preserving Natural Language Processing has risen in popularity, particularly at a time when concerns about privacy grow with the proliferation of Large Language Models. One solution consistently appearing in recent literature has
Externí odkaz:
http://arxiv.org/abs/2410.00751
Autor:
Schneider, Phillip, Matthes, Florian
Traditional search methods primarily depend on string matches, while semantic search targets concept-based matches by recognizing underlying intents and contextual meanings of search terms. Semantic search is particularly beneficial for discovering s
Externí odkaz:
http://arxiv.org/abs/2410.00427
Knowledge models are fundamental to dialogue systems for enabling conversational interactions, which require handling domain-specific knowledge. Ensuring effective communication in information-providing conversations entails aligning user understandi
Externí odkaz:
http://arxiv.org/abs/2408.01088
The task of $\textit{keyword extraction}$ is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document classification. While recent methods have proven to be quite effective
Externí odkaz:
http://arxiv.org/abs/2407.14085
The MEV-Boost block auction contributes approximately 90% of all Ethereum blocks. Between October 2023 and March 2024, only three builders produced 80% of them, highlighting the concentration of power within the block builder market. To foster compet
Externí odkaz:
http://arxiv.org/abs/2407.13931
Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses their abilities to easily adapt to different domains. We evaluate the domain adaptation abilities of a wide range of
Externí odkaz:
http://arxiv.org/abs/2407.11591
Autor:
Rockstrok, Sarah, Frenzel, Patrick, Matthes, Daniel, Schubert, Kay, Wollburg, David, Fuchs, Mirco
Assessing an athlete's performance in canoe sprint is often established by measuring a variety of kinematic parameters during training sessions. Many of these parameters are related to single or multiple paddle stroke cycles. Determining on- and offs
Externí odkaz:
http://arxiv.org/abs/2407.08395
Towards Optimizing and Evaluating a Retrieval Augmented QA Chatbot using LLMs with Human in the Loop
Large Language Models have found application in various mundane and repetitive tasks including Human Resource (HR) support. We worked with the domain experts of SAP SE to develop an HR support chatbot as an efficient and effective tool for addressing
Externí odkaz:
http://arxiv.org/abs/2407.05925