Yona, Gal

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

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

Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

Yona, G., Aharoni, R., & Geva, M. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 18

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

Pathways on the Image Manifold: Image Editing via Video Generation

Rotstein, N., Yona, G., Silver, D., Velich, R., Bensaïd, D., & Kimmel, R. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 5

SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge

Haas, L., Yona, G., D'Antonio, G., Goldshtein, S., & Das, D. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1