• “Calibration-Tuning: Teaching Large Language Models to Know What They Don’t Know”. Sanyam Kapoor, Nate Gruver, Manley Roberts, Arka Pal, Samuel Dooley, Micah Goldblum and Andrew Gordon Wilson

• “Context Tuning for Retrieval Augmented Generation”. Raviteja Anantha and Danil Vodianik.

• “Optimizing Relation Extraction in Medical Texts through Active Learning: A Comparative Analysis of Trade-offs”. Siting Liang, Pablo Valdunciel Sánchez and Daniel Sonntag

• “Linguistic Obfuscation Attacks and Large Language Model Uncertainty”. Sebastian Steindl, Ulrich Schäfer, Bernd Ludwig and Patrick Levi

• “Aligning Uncertainty: Leveraging LLMs to Analyze Uncertainty Transfer in Text Summarization”. Zahra Kolagar and Alessandra Zarcone

• “How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?”. Kazuma Hashimoto, Iftekhar Naim and Karthik Raman

• “Efficiently Acquiring Human Feedback with Bayesian Deep Learning”. Haishuo Fang, Jeet Gor and Edwin Simpson

• “Order Effects in Annotation Tasks: Further Evidence of Annotation Sensitivity”. Jacob Beck, Stephanie Eckman, Bolei Ma, Rob Chew and Frauke Kreuter

• “The Effect of Generalisation on the Inadequacy of the Mode”. Bryan Eikema

• “Uncertainty Resolution in Misinformation Detection”. Yury Orlovskiy, Camille Thibault, Anne Imouza, Jean-François Godbout, Reihaneh Rabbany and Kellin Pelrine

• “Don’t Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations”. Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar, Anweasha Saha, Zihao He, Ashwin Rao, Fred Morstatter and Kristina Lerman

• “Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation”. Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany and Kellin Pelrine

• “Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models”. Adarsa Sivaprasad and Ehud Reiter