6 citations · 9 across the 15 of their papers we have counts for
4 papers · 2 filters
Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang +8
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in rea…
Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
Sungmin Kang, Yavuz Faruk Bakman, Duygu Nur Yaldiz +2
The rapid advancement of large language models (LLMs) has transformed the landscape of natural language processing, enabling breakthroughs across a wide range of areas including qu…
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
Duygu Nur Yaldiz, Yavuz Faruk Bakman, Sungmin Kang +9
Generative Large Language Models (LLMs)inevitably produce untruthful responses. Accurately predicting the truthfulness of these outputs is critical, especially in high-stakes setti…
Un-considering Contextual Information: Assessing LLMs' Understanding of Indexical Elements
Metehan Oguz, Yavuz Bakman, Duygu Nur Yaldiz
Large Language Models (LLMs) have demonstrated impressive performances in tasks related to coreference resolution. However, previous studies mostly assessed LLM performance on core…