Search Results for author: Artem Spector

Found 7 papers, 2 papers with code

Conversational Prompt Engineering

no code implementations8 Aug 2024 Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim

We propose Conversational Prompt Engineering (CPE), a user-friendly tool that helps users create personalized prompts for their specific tasks.

Prompt Engineering

Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications

no code implementations25 Jul 2024 Alon Halfon, Shai Gretz, Ofir Arviv, Artem Spector, Orith Toledo-Ronen, Yoav Katz, Liat Ein-Dor, Michal Shmueli-Scheuer, Noam Slonim

Here, we provide recommended HP configurations for practical use-cases that represent a better starting point for practitioners, when considering two SOTA LLMs and two commonly used tuning methods.

Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis

1 code implementation6 Jan 2022 Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim

In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks.

Continual Pretraining Sentiment Analysis

Multilingual Argument Mining: Datasets and Analysis

no code implementations Findings of the Association for Computational Linguistics 2020 Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.

Argument Mining Machine Translation +3

Learning Concept Abstractness Using Weak Supervision

no code implementations EMNLP 2018 Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim

We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data.

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