Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Perlitz, Yotam"'
Consider a scenario where a harmfulness detection metric is employed by a system to filter unsafe responses generated by a Large Language Model. When analyzing individual harmful and unethical prompt-response pairs, the metric correctly classifies ea
Externí odkaz:
http://arxiv.org/abs/2408.12259
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
We introduce Holmes, a benchmark to assess the linguistic competence of language models (LMs) - their ability to grasp linguistic phenomena. Unlike prior prompting-based evaluations, Holmes assesses the linguistic competence of LMs via their internal
Externí odkaz:
http://arxiv.org/abs/2404.18923
Autor:
Bandel, Elron, Perlitz, Yotam, Venezian, Elad, Friedman-Melamed, Roni, Arviv, Ofir, Orbach, Matan, Don-Yehyia, Shachar, Sheinwald, Dafna, Gera, Ariel, Choshen, Leshem, Shmueli-Scheuer, Michal, Katz, Yoav
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations. The escalating complexity, involving system prom
Externí odkaz:
http://arxiv.org/abs/2401.14019
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:
Perlitz, Yotam, Gera, Ariel, Shmueli-Scheuer, Michal, Sheinwald, Dafna, Slonim, Noam, Ein-Dor, Liat
The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled data due to the extremely expensive and time-consuming process involved in manual annotation. A natural approach for coping with this problem is active learning
Externí odkaz:
http://arxiv.org/abs/2305.15040
We present nBIIG, a neural Business Intelligence (BI) Insights Generation system. Given a table, our system applies various analyses to create corresponding RDF representations, and then uses a neural model to generate fluent textual insights out of
Externí odkaz:
http://arxiv.org/abs/2211.04417
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
Generating natural language statements to convey logical inferences from tabular data (i.e., Logical NLG) is a process with one input and a variety of valid outputs. This characteristic underscores the need for a method to produce a diverse set of va
Externí odkaz:
http://arxiv.org/abs/2205.10938
General object detectors use powerful backbones that uniformly extract features from images for enabling detection of a vast amount of object types. However, utilization of such backbones in object detection applications developed for specific object
Externí odkaz:
http://arxiv.org/abs/2107.10050