Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Gretz, Shai"'
Autor:
Ein-Dor, Liat, Toledo-Ronen, Orith, Spector, Artem, Gretz, Shai, Dankin, Lena, Halfon, Alon, Katz, Yoav, Slonim, Noam
Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often tedious and time-consuming, requiring significant expertise, limiting its widespread
Externí odkaz:
http://arxiv.org/abs/2408.04560
Autor:
Halfon, Alon, Gretz, Shai, Arviv, Ofir, Spector, Artem, Toledo-Ronen, Orith, Katz, Yoav, Ein-Dor, Liat, Shmueli-Scheuer, Michal, Slonim, Noam
Fine-tuning Large Language Models (LLMs) is an effective method to enhance their performance on downstream tasks. However, choosing the appropriate setting of tuning hyperparameters (HPs) is a labor-intensive and computationally expensive process. He
Externí odkaz:
http://arxiv.org/abs/2407.18990
Autor:
Gretz, Shai, Toledo, Assaf, Friedman, Roni, Lahav, Dan, Weeks, Rose, Bar-Zeev, Naor, Sedoc, João, Sangha, Pooja, Katz, Yoav, Slonim, Noam
The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue sys
Externí odkaz:
http://arxiv.org/abs/2205.11966
Autor:
Friedman, Roni, Sedoc, João, Gretz, Shai, Toledo, Assaf, Weeks, Rose, Bar-Zeev, Naor, Katz, Yoav, Slonim, Noam
Public trust in medical information is crucial for successful application of public health policies such as vaccine uptake. This is especially true when the information is offered remotely, by chatbots, which have become increasingly popular in recen
Externí odkaz:
http://arxiv.org/abs/2205.12240
Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types
Externí odkaz:
http://arxiv.org/abs/2010.06185
One of the most impressive human endeavors of the past two decades is the collection and categorization of human knowledge in the free and accessible format that is Wikipedia. In this work we ask what makes a term worthy of entering this edifice of k
Externí odkaz:
http://arxiv.org/abs/2009.08240
Autor:
Gretz, Shai, Friedman, Roni, Cohen-Karlik, Edo, Toledo, Assaf, Lahav, Dan, Aharonov, Ranit, Slonim, Noam
Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging task of argument quality ranking. To this end, we created a corpus of
Externí odkaz:
http://arxiv.org/abs/1911.11408
Autor:
Toledo, Assaf, Gretz, Shai, Cohen-Karlik, Edo, Friedman, Roni, Venezian, Elad, Lahav, Dan, Jacovi, Michal, Aharonov, Ranit, Slonim, Noam
We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality.
Externí odkaz:
http://arxiv.org/abs/1909.01007
Autor:
Lavee, Tamar, Orbach, Matan, Kotlerman, Lili, Kantor, Yoav, Gretz, Shai, Dankin, Lena, Mirkin, Shachar, Jacovi, Michal, Bilu, Yonatan, Aharonov, Ranit, Slonim, Noam
Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a cor
Externí odkaz:
http://arxiv.org/abs/1907.11889
Publikováno v:
Language Resources and Evaluation, 2015 Jan 01. 49(1), 107-145.
Externí odkaz:
http://www.jstor.org/stable/24709997