Gekhman, Zorik

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

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

Confidence Improves Self-Consistency in LLMs

Taubenfeld, A., Sheffer, T., Ofek, E., Feder, A., Goldstein, A., Gekhman, Z., & Yona, G. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method

Gekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics

Topic ModelingSpeech and dialogue systems

Cited by 9