Mesut Ceylan, Arda Baris Basaran, Elgar Fleisch, Filipe Barata
Large language models (LLMs) are increasingly capable, yet they produce wrong answers indistinguishable from correct ones. In question answering (QA), such an error propagates unnoticed into downstream tasks. Perturbation-based uncertainty quantification (UQ) addresses this by probing a model over a set of input rewrites and reading uncertainty from how much its predictions vary. Yet this set is usually built generically, by free paraphrasing, token-level perturbation, or adversarial search. Such rewrites leave two things uncontrolled that matter in QA: what kind of change is made to the context, and how explicitly the context supports the answer. When neither is controlled, the composition of the set is determined by the generator rather than by design, and the estimate inherits whatever that generator happens to produce. Quality-diversity (QD) search is built to cover a predefined exploration space, so we recast input perturbation as a QD problem and propose \emph{neighborhood elites (NE)}, a neighborhood constructor that spreads answer-preserving rewrites across two axes, the kind of edit and the explicitness of support. We compare NE against three other neighborhood constructors, holding the editor, QA model, uncertainty measure and search objective fixed, and against three scores read from the original context. Across five extractive-QA datasets and five instruction-tuned LLMs, NE improves failure detection against every other constructor in at least 24 of the 25 dataset-model pairs, reaching 0.726 AUROC and 0.211 AUPRC lift over the base rate. Additionally, NE detects failure better than semantic entropy computed over decoder samples, by 0.074 AUROC and 0.062 AUPRC lift, and this advantage widens as the neighborhood grows.
Arda Baris Basaran, Mesut Ceylan, Elgar Fleisch, Filipe Barata
Vision-language anomaly detectors describe normal and abnormal appearance with text, and their gains are commonly credited to the semantic content of these descriptions. This attribution is plausible in the zero-shot setting, but few- and full-shot detectors interpose a trained visual adapter, and in that setting it has not been isolated. Changing a description changes both its meaning and its geometry, so accuracy comparisons cannot separate the two. We ask what a fixed pair of text embeddings, or anchors, contributes to such a detector: semantic content, effective scale, or joint anchor geometry. For a single anchor pair, the answer is effective scale alone. The score reads the anchors only through their difference, and a trained adapter can rotate to follow any replacement direction, leaving that direction, and with it the linguistic content, unidentifiable. We prove that replacement anchors with the same difference-vector length permit exactly the same probability maps and attainable losses. Across seven medical datasets, three vision backbones, both supervision regimes, and three adapter families, scale-matched random and orthogonal anchors track correct prompts; breaking the scale match opens a gap that restoring it closes without text. The same pattern holds in MVFA and when MadCLIP's learned prompts are replaced by a text-free learned scale. When one adapter serves many categories, the answer expands to their joint arrangement. Rotating all category axes together changes nothing, as predicted. For the tested CLIP prompts, these axes are nearly collinear, and meaning-free rotations that spread them apart improve performance on both industrial benchmarks. Even under frozen zero-shot transfer, a random anchor frame transfers as well as the prompt frame: coordinate consistency, not meaning. What these detectors read from their prompts is scale and arrangement, not semantic content.