Zobrazeno 1 - 10
of 2 153
pro vyhledávání: '"A. P. Eger"'
Autor:
Larionov, Daniil, Eger, Steffen
Evaluating the quality of machine-generated natural language content is a challenging task in Natural Language Processing (NLP). Recently, large language models (LLMs) like GPT-4 have been employed for this purpose, but they are computationally expen
Externí odkaz:
http://arxiv.org/abs/2412.16120
Autor:
Yuan, Shuzhou, Sun, Jingyi, Zhang, Ran, Färber, Michael, Eger, Steffen, Atanasova, Pepa, Augenstein, Isabelle
Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model's reasoning about its predictions. However, recent work has questioned the faithfulness of NLEs, as they may not accurately reflect the mode
Externí odkaz:
http://arxiv.org/abs/2412.12318
Autor:
Zhang, Leixin, Eger, Steffen, Cheng, Yinjie, Zhai, Weihe, Belouadi, Jonas, Leiter, Christoph, Ponzetto, Simone Paolo, Moafian, Fahimeh, Zhao, Zhixue
Multimodal large language models (LLMs) have demonstrated impressive capabilities in generating high-quality images from textual instructions. However, their performance in generating scientific images--a critical application for accelerating scienti
Externí odkaz:
http://arxiv.org/abs/2412.02368
Autor:
Leiter, Christoph, Belouadi, Jonas, Chen, Yanran, Zhang, Ran, Larionov, Daniil, Kostikova, Aida, Eger, Steffen
The NLLG (Natural Language Learning & Generation) arXiv reports assist in navigating the rapidly evolving landscape of NLP and AI research across cs.CL, cs.CV, cs.AI, and cs.LG categories. This fourth installment captures a transformative period in A
Externí odkaz:
http://arxiv.org/abs/2412.12121
Recent research has focused on literary machine translation (MT) as a new challenge in MT. However, the evaluation of literary MT remains an open problem. We contribute to this ongoing discussion by introducing LITEVAL-CORPUS, a paragraph-level paral
Externí odkaz:
http://arxiv.org/abs/2410.18697
Autor:
Zhang, Ran, Eger, Steffen
Despite substantial progress of large language models (LLMs) for automatic poetry generation, the generated poetry lacks diversity while the training process differs greatly from human learning. Under the rationale that the learning process of the po
Externí odkaz:
http://arxiv.org/abs/2409.03659
In therapeutic focused ultrasound (FUS), such as thermal ablation and hyperthermia, effective acousto-thermal manipulation requires precise targeting of complex geometries, sound wave propagation through irregular structures and selective focusing at
Externí odkaz:
http://arxiv.org/abs/2409.01323
Autor:
Leiter, Christoph, Eger, Steffen
Large language models (LLMs) have revolutionized NLP research. Notably, in-context learning enables their use as evaluation metrics for natural language generation, making them particularly advantageous in low-resource scenarios and time-restricted a
Externí odkaz:
http://arxiv.org/abs/2406.18528
Natural Language Generation (NLG), and more generally generative AI, are among the currently most impactful research fields. Creative NLG, such as automatic poetry generation, is a fascinating niche in this area. While most previous research has focu
Externí odkaz:
http://arxiv.org/abs/2406.15267
State-of-the-art trainable machine translation evaluation metrics like xCOMET achieve high correlation with human judgment but rely on large encoders (up to 10.7B parameters), making them computationally expensive and inaccessible to researchers with
Externí odkaz:
http://arxiv.org/abs/2406.14553