Baturalp Buyukates — accepted AI-conference papers

2025

Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs

Duygu Nur Yaldiz, Yavuz Faruk Bakman, Baturalp Buyukates, Chenyang Tao, Anil Ramakrishna, Dimitrios Dimitriadis +2

NAACL 2025findings

Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences. A significant subset of UE methods utilize token probabilities to assess uncertainty, aggregating multiple token probabilities into a single UE score using a scori…

Cited by 1 Source ↗PDF ↗Scholar ↗

2025

Reconsidering LLM Uncertainty Estimation Methods in the Wild

Yavuz Faruk Bakman, Duygu Nur Yaldiz, Sungmin Kang, Tuo Zhang, Baturalp Buyukates, Salman Avestimehr +1

ACL 2025long

Large Language Model (LLM) Uncertainty Estimation (UE) methods have become a crucial tool for detecting hallucinations in recent years. While numerous UE methods have been proposed, most existing studies evaluate them in isolated short-form QA settings using threshold-independent metrics such as AUR…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs

Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr

ACL 2024long

Generative Large Language Models (LLMs) are widely utilized for their excellence in various tasks. However, their tendency to produce inaccurate or misleading outputs poses a potential risk, particularly in high-stakes environments. Therefore, estimating the correctness of generative LLM outputs is…

Cited by 13 Source ↗PDF ↗code ↗Scholar ↗