信任但需验证：面向真实场景的程序化视觉语言模型评估
Trust but Verify: Programmatic VLM Evaluation in the Wild
AI总结：
本文提出PROVE基准，利用场景图程序化验证开放式视觉问答，并统一评估VLMs响应的有用性与真实性，发现多数模型难以平衡二者。
摘要：
视觉语言模型（VLMs）在回答视觉查询时经常生成看似合理但错误的响应。然而，在开放式查询的自由形式响应中可靠地量化此类幻觉的影响具有挑战性，因为这需要对响应中的每个论断进行视觉验证。我们提出程序化视觉语言模型评估（PROVE），这是一种用于评估视觉语言模型对开放式查询响应的新基准范式。为构建PROVE，我们向一个大语言模型（LLM）提供由超详细图像描述构建的高保真场景图表示，并提示其生成多样化的问答（QA）对，以及可在场景图对象上执行以验证每个QA对的程序。由此，我们构建了一个包含10.5k个具有挑战性但视觉上有依据的QA对的基准。接下来，为评估模型对PROVE中查询的自由形式响应，我们提出一种程序化评估策略，在统一的基于场景图的框架内同时衡量响应的有用性和真实性。我们在PROVE上对一系列视觉语言模型的有用性-真实性权衡进行了基准测试，发现实际上很少有模型能够在两者之间取得良好平衡。项目页面：\url{https://prove-explorer.netlify.app/}。
英文摘要：
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 (LLM) 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 visually grounded 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. Project page: \url{https://prove-explorer.netlify.app/}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
