Papers by Liat Ein-Dor
Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness (2026.acl-long)
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| Challenge: | Recent research suggests large language models encode meta-information about their own outputs. |
| Approach: | They investigate whether large language models possess similar privileged knowledge about answer correctness . they train correctness classifiers on question representations from a model’s hidden states and external models . |
| Outcome: | The proposed model outperforms peer-model models in factual knowledge tasks, but shows no advantage in math reasoning. |
Efficient Benchmarking (of Language Models) (2024.naacl-long)
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Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen
| Challenge: | Efficient Benchmarking is a method for reducing computation costs of LM evaluation without compromising reliability. |
| Approach: | They propose to reduce the computation costs of LM evaluation without compromising reliability by using a new measure - Decision Impact on Reliability. |
| Outcome: | The proposed benchmarks reduce computation costs by x100 or more, while maintaining reliability. |
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs (2026.findings-acl)
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| Challenge: | Existing representations of hallucinations limit the types of errors that can be expressed, so we propose a new representation based on free-form textual descriptions, capturing the full range of possible errors. |
| Approach: | They propose a benchmark for localizing hallucinations using LLMs with a human annotation of over 1,000 examples and a protocol to verify its quality in a humans evaluation. |
| Outcome: | The proposed representation captures the full range of possible errors, and the best model achieves an F1 score of 0.67. |
Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
Zero-shot Topical Text Classification with LLMs - an Experimental Study (2023.findings-emnlp)
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Shai Gretz, Alon Halfon, Ilya Shnayderman, Orith Toledo-Ronen, Artem Spector, Lena Dankin, Yannis Katsis, Ofir Arviv, Yoav Katz, Noam Slonim, Liat Ein-Dor
| Challenge: | Topical text classification is an ancient, yet timely research area in natural language processing. |
| Approach: | They compare the zero-shot performance of a variety of LMs over a large dataset of 23 publicly available TTC datasets. |
| Outcome: | The proposed models outperform their counterparts over a large dataset and show that they perform better in a zero-shot scenario. |
Multi-Domain Explainability of Preferences (2025.emnlp-main)
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| Challenge: | Existing methods for generating concept-based explanations of preferences are poorly understood. |
| Approach: | They propose a method for generating local and global concept-based explanations of preferences across multiple domains using an LLM. |
| Outcome: | The proposed method outperforms baselines while also being explainable. |
Advances in Debating Technologies: Building AI That Can Debate Humans (2021.acl-tutorials)
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| Challenge: | This tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as "computational technologies developed directly to enhance, support, and engage with human debating" the tutorial provides a holistic view of a debated system, and discusses practical applications and future challenges of debation technologies. |
| Approach: | They present a tutorial on Debating Technologies, a sub-field of computational argumentation . they introduce Project Debater, which is the first AI system to debate human experts . |
| Outcome: | The project Debater is the first AI system to debate human experts on complex topics. |
Active Learning for Natural Language Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to NLG are limited by the lack of annotated data. |
| Approach: | They propose to use active learning to reduce the cost of manual annotation to improve annotation efficiency by selecting the most informative examples to label. |
| Outcome: | The proposed approach surpasses baseline of random example selection in some cases but not in others. |
Zero-Shot Text Classification with Self-Training (2022.emnlp-main)
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| Challenge: | Recent advances in large pretrained language models have increased attention to zero-shot text classification. |
| Approach: | They propose a plug-and-play method to bridge this gap by requiring only class names along with an unlabeled dataset. |
| Outcome: | The proposed model can be trained on a natural language inference dataset and performs on dozens of unseen tasks without the need for domain expertise or trial and error. |
Label-Efficient Model Selection for Text Generation (2024.acl-long)
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| Challenge: | Model selection for a given task can entail extensive annotation of the quality of outputs of different models. |
| Approach: | They propose a model-agnostic method to make an informed decision between candidate text generation models based on preference annotations. |
| Outcome: | The proposed method reduces the required number of annotations by up to 75% while maintaining high evaluation reliability. |
Active Learning for BERT: An Empirical Study (2020.emnlp-main)
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Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim
| Challenge: | Existing approaches to deal with data scarcity are active learning (AL) and pre-trained models are not being considered. |
| Approach: | They propose to use active learning techniques to cope with data scarcity in binary text classification scenarios where the annotation budget is very small and the data is often skewed. |
| Outcome: | The proposed methods improve BERT performance in binary text classification scenarios where the annotation budget is very small and the data is often skewed. |
Quality Controlled Paraphrase Generation (2022.acl-long)
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| Challenge: | Recent studies have shown that high quality paraphrases are difficult to generate because of their low flexibility and scalability. |
| Approach: | They propose a quality-guided controlled paraphrase generation model that allows directly controlling the quality dimensions of the generated paraphrase. |
| Outcome: | The proposed method generates paraphrases which maintain original meaning while achieving higher diversity than the uncontrolled baseline. |