Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Shnarch, Eyal"'
Autor:
Perlitz, Yotam, Gera, Ariel, Arviv, Ofir, Yehudai, Asaf, Bandel, Elron, Shnarch, Eyal, Shmueli-Scheuer, Michal, Choshen, Leshem
Recent advancements in Language Models (LMs) have catalyzed the creation of multiple benchmarks, designed to assess these models' general capabilities. A crucial task, however, is assessing the validity of the benchmarks themselves. This is most comm
Externí odkaz:
http://arxiv.org/abs/2407.13696
Autor:
Ashury-Tahan, Shir, Gera, Ariel, Sznajder, Benjamin, Choshen, Leshem, Ein-Dor, Liat, Shnarch, Eyal
Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models. We introduce DiffUse, an efficient method to make an informed decision between candidate text generation model
Externí odkaz:
http://arxiv.org/abs/2402.07891
Autor:
Yehudai, Asaf, Carmeli, Boaz, Mass, Yosi, Arviv, Ofir, Mills, Nathaniel, Toledo, Assaf, Shnarch, Eyal, Choshen, Leshem
The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel method for automatically generating high-quality content-grounded dat
Externí odkaz:
http://arxiv.org/abs/2401.14367
Autor:
Perlitz, Yotam, Bandel, Elron, Gera, Ariel, Arviv, Ofir, Ein-Dor, Liat, Shnarch, Eyal, Slonim, Noam, Shmueli-Scheuer, Michal, Choshen, Leshem
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities. Such benchmarks are associated with massive computational costs, extending to thousands of GPU
Externí odkaz:
http://arxiv.org/abs/2308.11696
Autor:
Gera, Ariel, Friedman, Roni, Arviv, Ofir, Gunasekara, Chulaka, Sznajder, Benjamin, Slonim, Noam, Shnarch, Eyal
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative. In this work, we argue that due to the g
Externí odkaz:
http://arxiv.org/abs/2305.01628
Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot classifiers due to their promi
Externí odkaz:
http://arxiv.org/abs/2210.17541
Autor:
Shnarch, Eyal, Halfon, Alon, Gera, Ariel, Danilevsky, Marina, Katsis, Yannis, Choshen, Leshem, Cooper, Martin Santillan, Epelboim, Dina, Zhang, Zheng, Wang, Dakuo, Yip, Lucy, Ein-Dor, Liat, Dankin, Lena, Shnayderman, Ilya, Aharonov, Ranit, Li, Yunyao, Liberman, Naftali, Slesarev, Philip Levin, Newton, Gwilym, Ofek-Koifman, Shila, Slonim, Noam, Katz, Yoav
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML knowledge, which poses a significant barrier for many potential users. T
Externí odkaz:
http://arxiv.org/abs/2208.01483
Autor:
Sznajder, Benjamin, Gunasekara, Chulaka, Lev, Guy, Joshi, Sachin, Shnarch, Eyal, Slonim, Noam
Many organizations require their customer-care agents to manually summarize their conversations with customers. These summaries are vital for decision making purposes of the organizations. The perspective of the summary that is required to be created
Externí odkaz:
http://arxiv.org/abs/2203.15590
Autor:
Shnarch, Eyal, Gera, Ariel, Halfon, Alon, Dankin, Lena, Choshen, Leshem, Aharonov, Ranit, Slonim, Noam
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained models, such as BERT, for a target classification task, is prone to produce
Externí odkaz:
http://arxiv.org/abs/2203.10581
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for extracting patterns from textua
Externí odkaz:
http://arxiv.org/abs/2104.03958