1 citations · 1 across the 6 of their papers we have counts for
6 papers
When Text and Numbers Disagree: Evidence Arbitration in Large Language Models
Mattia Carletti, Edward Phillips, Fredrik K. Gustafsson +5
Large language models (LLMs) are increasingly used in settings where textual summaries, numerical observations, and external tool outputs may provide conflicting evidence. We study…
Visual-Prompt Guided Wildlife Instance-Level Recognition
Mufhumudzi Muthivhi, Jiahao Huo, Terence van Zyl +1
Fine-grained wildlife re-identification remains a challenging area in research. Current state-of-the-art approaches apply a detection and re-identification pipeline. We propose a o…
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence
Sean Wu, Fredrik K. Gustafsson, Edward Phillips +3
Large language models (LLMs) often produce confident but incorrect answers in settings where abstention would be safer. Standard evaluation protocols, however, require a response a…
Semantic Self-Distillation for Language Model Uncertainty
Edward Phillips, Sean Wu, Fredrik K. Gustafsson +2
Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the…
Nearest-Class Mean and Logits Agreement for Wildlife Open-Set Recognition
Jiahao Huo, Mufhumudzi Muthivhi, Terence L. van Zyl +1
Current state-of-the-art Wildlife classification models are trained under the closed world setting. When exposed to unknown classes, they remain overconfident in their predictions.…
Improving Wildlife Out-of-Distribution Detection: Africas Big Five
Mufhumudzi Muthivhi, Jiahao Huo, Fredrik Gustafsson +1
Mitigating human-wildlife conflict seeks to resolve unwanted encounters between these parties. Computer Vision provides a solution to identifying individuals that might escalate in…