Hi! My name is Gal Yona. I am currently a Research Scientist at Google Research Tel Aviv, where I work on making large language models more robust, factual and trustworthy. Before Google, I completed a PhD in Computer Science at the Weizmann Institute of Science, under the supervision of Guy Rothblum, where I focused on formalizing fairness & non-discrimination in machine learning algorithms.
You can contact me at: gal.yona at gmail.com
For an up to date list, check out my Google Scholar.
Confidence Improves Self-Consistency in LLMs
Amir Taubenfeld, Tom Sheffer, Eran Ofek, Amir Feder, Ariel Goldstein, Zorik Gekhman and Gal Yona
preprint
[paper]
Keep Guessing? When Considering Inference Scaling, Mind the Baselines
Gal Yona, Or Honovich, Omer Levy and Roee Aharoni
Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL) 2025 Findings
[paper]
Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?
Gal Yona, Roee Aharoni and Mor Geva
Conference on Empirical Methods in Natural Language Processing (EMNLP) 2024
[paper]
Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart and Jonathan Herzig
Conference on Empirical Methods in Natural Language Processing (EMNLP) 2024
[paper]
Narrowing the Knowledge Evaluation Gap: Open-Domain Question Answering with Multi-Granularity Answers
Gal Yona, Roee Aharoni and Mor Geva
Association for Computational Linguistics (ACL) 2024
[paper]
Surfacing Biases in Large Language Models using Contrastive Input Decoding
Gal Yona, Or Honovich, Itay Laish and Roee Aharoni
preprint
[paper]
Malign Overfitting: Interpolation Can Provably Preclude Invariance
Yoav Wald, Gal Yona, Uri Shalit and Yair Carmon
International Conference on Learning Representations (ICLR) 2023
[paper]
Decision-Making under Miscalibration
Guy N. Rothblum and Gal Yona
Innovations in Theoretical Computer Science (ITCS) 2023
[paper] [video] [code]
Useful Confidence Measures: Beyond the Max Score
Gal Yona, Amir Feder and Itay Laish
Workshop on Distribution Shifts at Neurips 2022
[paper]
Active Learning with Label Comparisons
Gal Yona, Shay Moran, Gal Elidan and Amir Globerson
Uncertainty in Artificial Intelligence (UAI) 2022
[paper] [poster]
Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum and Gal Yona
Algorithmic Learning Theory (ALT) 2022
[paper]
On Fairness and Stability in Two-Sided Matchings
Gili Karni, Guy N. Rothblum and Gal Yona
Innovations in Theoretical Computer Science (ITCS) 2022
[paper]
Revisiting Sanity Checks for Saliency Maps
Gal Yona and Daniel Greenfeld
Workshop on eXplainable AI approaches for debugging and diagnosis at Neurips 2021
[paper] [poster]
Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification
Guy N. Rothblum and Gal Yona
ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) 2021 (Poster)
[paper] [poster]
Multi-group Agnostic PAC Learning
Guy N. Rothblum and Gal Yona
International Conference on Machine Learning (ICML) 2021
[paper] [video]
Who’s responsible? Jointly quantifying the contribution of the learning algorithm and training data
Gal Yona, Amirata Ghorbani and James Zou
Artificial Intelligence, Ethics and Society (AIES) 2021
[paper] [poster]
Outcome Indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum and Gal Yona \
Symposium on Theory of Computing (STOC) 2021
[paper] [video]
Addressing bias in prediction models by improving subpopulation calibration
Noam Barda, Gal Yona, Guy N Rothblum, Philip Greenland, Morton Leibowitz, Ran Balicer, Eitan Bachmat and Noa Dagan
Journal of the American Medical Informatics Association (JAMIA) 2020
[paper]
Developing a COVID-19 mortality risk prediction model when individual-level data are not available
Noam Barda, Dan Riesel, Amichay Akriv, Joseph Levy, Uriah Finkel, Gal Yona, Daniel Greenfeld, Shimon Sheiba, Jonathan Somer, Eitan Bachmat, Guy N Rothblum, Uri Shalit, Doron Netzer, Ran Balicer and Noa Dagan
Nature Communications 2020
[paper]
Preference-Informed Fairness
Michael P. Kim, Aleksandra Korolova, Guy N. Rothblum and Gal Yona
Innovations in Theoretical Computer Science (ITCS) 2020
[paper]
[talk]
Evidence-Based Rankings
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum and Gal Yona
Foundations of Computer Science (FOCS) 2019
[paper]
Probably Approximately Metric Fair Learning
Guy N. Rothblum and Gal Yona
International Conference on Machine Learning (ICML) 2018
[paper] [talk]