Search Results for author: Liat Ein-Dor

Found 19 papers, 6 papers with code

Active Learning for BERT: An Empirical Study

1 code implementation EMNLP 2020 Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim

Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets.

Active Learning Binary text classification +3

Multi-Domain Explainability of Preferences

no code implementations26 May 2025 Nitay Calderon, Liat Ein-Dor, Roi Reichart

In this work, we propose a fully automated end-to-end method for generating local and global concept-based explanations of preferences across multiple domains.

WildIFEval: Instruction Following in the Wild

1 code implementation9 Mar 2025 Gili Lior, Asaf Yehudai, Ariel Gera, Liat Ein-Dor

In this work, we introduce WildIFEval - a large-scale dataset of 12K real user instructions with diverse, multi-constraint conditions.

Instruction Following

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.

Label-Efficient Model Selection for Text Generation

no code implementations12 Feb 2024 Shir Ashury-Tahan, Ariel Gera, Benjamin Sznajder, Leshem Choshen, Liat Ein-Dor, Eyal Shnarch

Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models.

model Model Selection +1

Efficient Benchmarking of Language Models

no code implementations22 Aug 2023 Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen

The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.

Benchmarking

Active Learning for Natural Language Generation

no code implementations24 May 2023 Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer, Dafna Sheinwald, Noam Slonim, Liat Ein-Dor

In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model.

Active Learning text-classification +2

SimpleStyle: An Adaptable Style Transfer Approach

no code implementations20 Dec 2022 Elron Bandel, Yoav Katz, Noam Slonim, Liat Ein-Dor

We offer our protocol as a simple yet strong baseline for works that wish to make incremental advancements in the field of attribute controlled text rewriting.

Attribute Denoising +2

Zero-Shot Text Classification with Self-Training

1 code implementation31 Oct 2022 Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim

Recent advances in large pretrained language models have increased attention to zero-shot text classification.

Natural Language Inference text-classification +2

Diversity Enhanced Table-to-Text Generation via Type Control

no code implementations22 May 2022 Yotam Perlitz, Liat Ein-Dor, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer

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.

Diversity Table-to-Text Generation +2

Quality Controlled Paraphrase Generation

1 code implementation ACL 2022 Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor

Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.

Diversity Paraphrase Generation +1

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

Advances in Debating Technologies: Building AI That Can Debate Humans

no code implementations ACL 2021 Roy Bar-Haim, Liat Ein-Dor, Matan Orbach, Elad Venezian, Noam Slonim

We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components.

Argument Mining Stance Classification

Financial Event Extraction Using Wikipedia-Based Weak Supervision

no code implementations WS 2019 Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.

BIG-bench Machine Learning Event Extraction

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