Titlebook: Natural Language Processing and Information Systems; 26th International C Elisabeth Métais,Farid Meziane,Epaminondas Kapetan Conference pro

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Cross-Domain Transfer of Generative Explanations Using Text-to-Text Modelsa low-resource task in another domain. Our proposed three-step training procedure improves explanation quality by up to 7% and avoids sacrificing classification performance on the downstream task, while at the same time reducing the need for human annotations.

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Word Embedding-Based Topic Similarity Measurese-of-the-art topic similarity measures and propose novel metrics based on word embeddings. The proposed measures can overcome some limitations of the existing approaches, highlighting good capabilities in terms of several topic performance measures on benchmark datasets.

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0302-9743 are organized in the following topical sections: role of learning; methodological approaches; semantic relations; classification; sentiment analysis; social media; linking documents; multimodality; applications. .978-3-030-80598-2978-3-030-80599-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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the process of learning about interpretation of seismic data. This multidisciplinary, integrated and practical approach to data evaluation will prove to be a valuable tool for students and young professionals, 978-3-319-79961-2978-3-319-26491-2

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Scaling Federated Learning for Fine-Tuning of Large Language Modelsng setting. While our findings suggest that the large sizes of the evaluated models are not generally prohibitive to federated training, we found that not all models handle federated averaging well. Most notably, DistilBERT converges significantly slower with larger numbers of clients, and under som

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