Choshen, Leshem

Q2: : Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

Honovich, O., Choshen, L., Aharoni, R., Neeman, E., Szpektor, I., & Abend, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsTopic Modeling

Cited by 113

Ties-merging: Resolving interference when merging models

Yadav, P., Tam, D., Choshen, L., Raffel, C., & Bansal, M. (2023) · Advances in Neural Information Processing Systems 36

Model-Driven Software Engineering TechniquesFormal Methods in Verification

Cited by 95

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., et al. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning

Topic ModelingNatural Language Processing Techniques

Cited by 74

Jump to Conclusions: Short-Cutting Transformers with Linear Transformations

Yom Din, A., Karidi, T., Choshen, L., & Geva, M. (2024) · Proceedings of the Language Resources and Evaluation Conference

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 61

Efficient multi-prompt evaluation of LLMs

Polo, F., Xu, R., Weber, L., Silva, M., Bhardwaj, O., Choshen, L., Oliveira, A. D., Sun, Y., & Yurochkin, M. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingNatural Language Processing Techniques

Cited by 60

On the Weaknesses of Reinforcement Learning for Neural Machine Translation

Choshen, L., Fox, L., Aizenbud, Z., & Abend, O. (2019) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 28

NumeroLogic: Number Encoding for Enhanced LLMs’ Numerical Reasoning

Schwartz, E., Choshen, L., Shtok, J., Doveh, S., Karlinsky, L., & Arbelle, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 14

Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

Warstadt, A., Choshen, L., Mueller, A., Williams, A., Wilcox, E., & Zhuang, C. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

Wilcox, E. G., Hu, M. Y., Mueller, A., Warstadt, A., Choshen, L., Zhuang, C., Williams, A., Cotterell, R., & Linzen, T. (2025) · Journal of Memory and Language

Natural Language Processing TechniquesTopic Modeling

Cited by 9

tinyBenchmarks: evaluating LLMs with fewer examples

Polo, F. M., Weber, L., Choshen, L., Sun, Y., Xu, G., & Yurochkin, M. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Efficient Benchmarking (of Language Models)

Perlitz, Y., Bandel, E., Gera, A., Arviv, O., Ein-Dor, L., Shnarch, E., Slonim, N., Shmueli-Scheuer, M., & Choshen, L. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 7

Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability

Akyürek, A. F., Akyürek, E., Choshen, L., Wijaya, D., & Andreas, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

Genie: Achieving Human Parity in Content-Grounded Datasets Generation

Yehudai, A., Carmeli, B., Mass, Y., Arviv, O., Mills, N., Toledo, A., Shnarch, E., & Choshen, L. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

Akhtar, M., Reuel, A., Soni, P., Ahuja, S., Ammanamanchi, P. S., Rawal, R., Zouhar, V., Yadav, S., Whitehouse, C., Ki, D., et al. (2026) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models

Ivanova, A. A., Sathe, A., Lipkin, B., Kumar, U., Radkani, S., Clark, T. H., Kauf, C., Hu, J., Pramod, R. T., Grand, G., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 2

Naturally Occurring Feedback is Common, Extractable and Useful

Don-Yehiya, S., Choshen, L., & Abend, O. (2024) · arXiv

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 1

Automated Discovery Has No Universally Superior Harness

Gupta, A., Lei, J., Lu, A., Anumanchipalli, G., & Choshen, L. (2026) · arXiv

Evolutionary Algorithms and ApplicationsAI-based Problem Solving and Planning

Cited by 0

Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data

Arviv, O., Greenewald, K., Perlitz, Y., Mulian, H., Shmueli-Scheuer, M., & Choshen, L. (2026) · Findings of the Association for Computational Linguistics: ACL 2026

Machine Learning and Data ClassificationSimulation Techniques and Applications

Cited by 0

Efficient Benchmarking (of Language Models)

Perlitz, Y., Bandel, E., Gera, A., Arviv, O., Ein-Dor, L., Shnarch, E., Slonim, N., Shmueli-Scheuer, M., & Choshen, L. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0