Gal Yona

Publications

For an up-to-date list, see my Google Scholar.

• SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
  Lukas Haas, Gal Yona, Giovanni D'Antonio, Sasha Goldshtein and Dipanjan Das
  Tech Report 2025
  [paper]
• MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
  Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, Idan Szpektor, Tim GJ Rudner and Arman Cohan
  Empirical Methods in Natural Language Processing (EMNLP) 2025
  [paper]
• Confidence Improves Self-Consistency in LLMs
  Amir Taubenfeld, Tom Sheffer, Eran Ofek, Amir Feder, Ariel Goldstein, Zorik Gekhman and Gal Yona
  Association for Computational Linguistics (ACL) 2025 Findings
  [paper]
• Keep Guessing? When Considering Inference Scaling, Mind the Baselines
  Gal Yona, Or Honovich, Omer Levy and Roee Aharoni
  North American 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
  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
  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
  [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]