Explicit Abstention Knobs for Predictable Reliability in Video Question Answering
This addresses the need for predictable reliability in high-stakes VLM deployments, though it is incremental as it builds on existing abstention methods.
The study investigated whether confidence-based abstention in video question answering provides reliable control over error rates, finding that it offers mechanistic control in-distribution but fails under distribution shift, with error rates reduced by up to 50% at high coverage.
High-stakes deployment of vision-language models (VLMs) requires selective prediction, where systems abstain when uncertain rather than risk costly errors. We investigate whether confidence-based abstention provides reliable control over error rates in video question answering, and whether that control remains robust under distribution shift. Using NExT-QA and Gemini 2.0 Flash, we establish two findings. First, confidence thresholding provides mechanistic control in-distribution. Sweeping threshold epsilon produces smooth risk-coverage tradeoffs, reducing error rates f