用大模型判断图文是否相关，准确率超人工基准。
LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model
- 基于多模态大模型构建图文相关性二分类评估框架
- 在多个任务上达到90%以上准确率，优于人工基准
- 适合需要快速评估图文生成质量的研究与产品团队
多模态生成AI通常需根据一种模态输入生成另一种模态的输出。图文相关性评估对于衡量响应质量或排序候选结果至关重要，其中二分类相关性评估（即“相关”与“不相关”）是基础问题。然而，由于文本格式多样且相关性定义因场景而异，该任务极具挑战性。我们发现多模态大模型（MLLMs）是构建此类评估器的理想选择，因其可灵活处理复杂文本格式并融入额外任务信息。本文提出首个基于MLLM的二分类图文相关性评估框架LLaVA-RE，沿用LLaVA架构，采用详细的任务指令和多模态上下文样本。此外，我们构建了一个覆盖多种任务的新二分类相关性数据集。实验结果验证了该框架的有效性。
原文摘要 · Abstract (English)
Multimodal generative AI usually involves generating image or text responses given inputs in another modality. The evaluation of image-text relevancy is essential for measuring response quality or ranking candidate responses. In particular, binary relevancy evaluation, i.e., ``Relevant'' vs. ``Not Relevant'', is a fundamental problem. However, this is a challenging task considering that texts have diverse formats and the definition of relevancy varies in different scenarios. We find that Multimodal Large Language Models (MLLMs) are an ideal choice to build such evaluators, as they can flexibly handle complex text formats and take in additional task information. In this paper, we present LLaVA-RE, a first attempt for binary image-text relevancy evaluation with MLLM. It follows the LLaVA architecture and adopts detailed task instructions and multimodal in-context samples. In addition, we propose a novel binary relevancy data set that covers various tasks. Experimental results validate the effectiveness of our framework.
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