Papers by Ofir Arviv
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 Analysis of Cross-Linguistic Syntactic Divergences (2020.acl-main)
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Dmitry Nikolaev, Ofir Arviv, Taelin Karidi, Neta Kenneth, Veronika Mitnik, Lilja Maria Saeboe, Omri Abend
| Challenge: | Existing work on quantifying the prevalence of syntactic divergences across languages has not been done. |
| Approach: | They propose a framework for extracting divergence patterns for any language pair from a parallel corpus building on Universal Dependencies. |
| Outcome: | The proposed framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. |
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI (2024.naacl-demo)
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Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman, Ofir Arviv, Matan Orbach, Shachar Don-Yehiya, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, Yoav Katz
| Challenge: | Textual data processing pipelines are tailored to specific datasets, task and model combinations. |
| Approach: | They propose a library for customizable textual data preparation and evaluation tailored to generative language models. |
| Outcome: | Unitxt is a library for customizable textual data preparation and evaluation tailored to generative language models. |
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data (2026.findings-acl)
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Ofir Arviv, Kristjan Greenewald, Yotam Perlitz, Hadar Mulian, Michal Shmueli-Scheuer, Leshem Choshen
| Challenge: | Current evaluation practices, typically employing fixed-size benchmarks, are inherently wasteful, continuing to the predetermined sample size even when the CI reaches 2.5, saving 80% of the evaluation cost. |
| Approach: | They propose an adaptive evaluation framework that combines sequential testing with stopping criteria tailored to common evaluation needs such as diminishing returns detection and minimum detectable effect size. |
| Outcome: | The proposed framework reduces computational cost and reliability while maintaining statistical significance. |
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. |
DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation (2025.findings-acl)
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Eliya Habba, Ofir Arviv, Itay Itzhak, Yotam Perlitz, Elron Bandel, Leshem Choshen, Michal Shmueli-Scheuer, Gabriel Stanovsky
| Challenge: | Recent work found that LLMs are sensitive to arbitrary prompt dimensions . this challenges traditional single-prompt evaluation practices . |
| Approach: | They present a large-scale dataset containing prompt perturbations of various evaluation benchmarks . they examine LLM sensitivity from an holistic perspective and assess the joint effects of perturbations along various dimensions . |
| Outcome: | The proposed dataset aims to democratize evaluation research and enable robust protocols . it includes more than 250M prompt perturbations and model outputs . |
Improving Cross-lingual Transfer through Subtree-aware Word Reordering (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual language models are not effective when dealing with less-represented languages. |
| Approach: | They propose a powerful reordering method that learns word-order patterns conditioned on the syntactic context from a small amount of annotated data. |
| Outcome: | The proposed method outperforms baselines on a variety of tasks and is effective in both zero-shot and few-shot scenarios. |
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers (2023.acl-long)
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Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch
| Challenge: | Existing approaches to apply language models to tasks that require intermediate representations are less informative. |
| Approach: | They propose a novel approach that utilizes the contrast between layers to improve text generation outputs. |
| Outcome: | The proposed approach mitigates degenerative behaviors of the model in open-ended generation, significantly improving the quality of generated texts. |
On the Relation between Syntactic Divergence and Zero-Shot Performance (2021.emnlp-main)
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| Challenge: | Recent advances in cross-lingual transfer methods have enabled significant advances in grammatical processing tasks. |
| Approach: | They examine the extent to which syntactic relations are preserved in translation and parsability in a zero-shot setting. |
| Outcome: | The proposed model is based on a translation task in English and a subset of a standard English RE benchmark translated to Russian and Korean. |