🧑‍🔬 Gal Yona

🏛️ Affiliations

🏢GoogleMountain View · United States2024–2026 · 10 paperscurrent🎓Weizmann Institute of ScienceRehovot · Israel2018–2023 · 18 papers🎓Technion – Israel Institute of TechnologyHaifa · Israel2024–2025 · 3 papersEmployment · M.Sc student, Computer Science

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words? (2024 · 40 citations)
   LLMs often state false facts in a confident, fluent tone, which can lead users to over-trust them. The authors argue that hedging in a response should reflect the model's own uncertainty, not external correctness. They define faithful response uncertainty, a per-example score equal to one minus the average gap…
2. SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge (2025 · 12 citations)
   OpenAI's SimpleQA became a standard test of whether LLMs can recall short facts from their own weights. It has flaws, though. Some reference answers are wrong, individual raters contributed clusters of near-identical questions, and dates, person names and science/technology topics dominate.
3. MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs (2025 · 6 citations)
   LLMs often state false claims in confident language, which leads users to over-trust them. This paper studies "faithful calibration": whether the decisiveness of a model's wording matches its intrinsic uncertainty. This differs from standard factual calibration, which ties confidence to accuracy. The authors…

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations? ↗arXiv:2405.05904 · no summary yetZorik Gekhman et al.2024 · 171 citations
2. Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?Conference on Empirical Methods in Natural Language Processing (EMNLP)Gal Yona, Roee Aharoni, Mor Geva2024 · 40 citations
3. SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric KnowledgearXiv:2509.07968Lukas Haas, Gal Yona, Giovanni D'Antonio, et al.2025 · 12 citations
4. MetaFaith: Faithful Natural Language Uncertainty Expression in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu, et al.2025 · 6 citations
5. Keep Guessing? When Considering Inference Scaling, Mind the Baselines ↗Findings of the Association for Computational Linguistics: NAACL 2025 · no summary yetGal Yona et al.2025

🤝 Co-authors here

8 co-authors with a page, the most papers together first.

• Arman Cohan
• Avi Caciularu
• Dipanjan Das
• Gabrielle Kaili-May Liu
• Idan Szpektor
• Lukas Haas
• Mor Geva
• Roee Aharoni

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2025, EMNLP 2024
• Findings of the Association for Computational Linguistics: NAACL 2025

🔗 Cited by

Papers of our library cite their works 128 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated 30 October 2026

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