Trust but Verify: Programmatic VLM Evaluation in the Wild
Keywords: vision-language models, evaluation, hallucinations
TL;DR: Reliable in-the-wild VLM benchmarking via programmatic verification & evaluation
Abstract: Vision-Language Models (VLMs) often generate plausible but incorrect responses to visual queries. However, reliably quantifying the effect of such hallucinations in free-form responses to open-ended queries is challenging as it requires visually verifying each claim within the response. We propose Programmatic VLM Evaluation (PROVE), a new benchmarking paradigm for evaluating VLM responses to open-ended queries. To construct PROVE, we provide a large language model with a high-fidelity scene-graph representation constructed from a hyper-detailed image caption, and prompt it to generate diverse question-answer (QA) pairs, as well as programs that can be executed over the scene graph object to _verify_ each QA pair. We thus construct a benchmark of 10.5k challenging but grounded visual QA pairs. Next, to evaluate free-form model responses to queries in PROVE, we propose a _programmatic_ evaluation strategy that measures both the helpfulness and truthfulness of a response within a unified scene graph-based framework. We benchmark the helpfulness-truthfulness trade-offs of a range of VLMs on PROVE, finding that very few are in-fact able to achieve a good balance between the two.
Primary Area: datasets and benchmarks
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Submission Number: 559
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