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UvA-DARE (Digital Academic Repository) A Deep Dive Into Cross-Dataset Entity Matching with Large and Small Language Models

Uncertainty Is Not a Safety Net for Clinical VQA, but Can It Anticipate Model Failure?

It is found that uncertainty on the unperturbed input reliably anticipates which predictions will collapse under NOTA, indicating that UE in current VLMs carries diagnostic information about model fragility.

Investigating Linear Probe Robustness to Linguistic Register, Medical Specialty, and Corpus Shifts in Medical QA

The truth direction is largely stable within the medical domain but breaks under some corpus shifts, and question format does not explain the break, which suggests that the signal a linear probe recovers is partly bound to dataset structure rather than to medical knowledge alone.

Explainability of decoder-only clinical large language models: A scoping review

Findings show that clinical LLM explainability has shifted toward fluent generative rationales, but evidence that such explanations reflect model reasoning remains limited, and three regulatory priorities are highlighted: prioritizing explanations that enable independent verification or logic auditing over plausibility...

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

The factors underlying performance differences across matcher architectures are clarified and motivate future research and benchmark designs that better disentangle architectural choices from model-level factors while explicitly evaluating distribution shift and cross-dataset transferability.