June 7, 2024, 4:51 a.m. | Tzuf Paz-Argaman, Itai Mondshine, Asaf Achi Mordechai, Reut Tsarfaty

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.03897v1 Announce Type: new
Abstract: While large language models (LLMs) excel in various natural language tasks in English, their performance in lower-resourced languages like Hebrew, especially for generative tasks such as abstractive summarization, remains unclear. The high morphological richness in Hebrew adds further challenges due to the ambiguity in sentence comprehension and the complexities in meaning construction. In this paper, we address this resource and evaluation gap by introducing HeSum, a novel benchmark specifically designed for abstractive text summarization in …

abstract arxiv challenges cs.ai cs.cl dataset english excel generative language language models languages large language large language models llms natural natural language novel performance summarization tasks text text summarization type while

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