Herzig, Jonathan

Learning To Retrieve Prompts for In-Context Learning

Rubin, O., Herzig, J., & Berant, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 374

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 86

Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Gekhman, Z., Yona, G., Aharoni, R., Eyal, M., Feder, A., Reichart, R., & Herzig, J. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 86

TRUE: Re-evaluating Factual Consistency Evaluation

Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 84

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

TRUE: Re-evaluating Factual Consistency Evaluation

Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022) · Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

Speech and dialogue systemsTopic Modeling

Cited by 56

TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models

Gekhman, Z., Herzig, J., Aharoni, R., Elkind, C., & Szpektor, I. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 33

Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models

Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., Soares, L. B., Ciaramita, M., Eisenstein, J., Ganchev, K., Herzig, J., et al. (2022) · arXiv

Topic ModelingAuthorship Attribution and Profiling

Cited by 30

A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

Jacovi, A., Bitton, Y., Bohnet, B., Herzig, J., Honovich, O., Tseng, M., Collins, M., Aharoni, R., & Geva, M. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 14