Benchmarks for multimodal search and retrieval-augmented generation (RAG) help developers evaluate how well systems handle tasks that combine multiple data types (text, images, audio) and integrate retrieval with generative models. These benchmarks measure performance in retrieving relevant information from diverse sources and generating accurate, context-aware outputs. While no single benchmark covers all scenarios, several established options focus on specific aspects of multimodal and RAG workflows.
For multimodal search, datasets like OK-VQA (Outside Knowledge Visual Question Answering) test systems that answer questions by analyzing images and external knowledge. For example, a question like “What animal is native to the habitat shown in this photo?” requires linking visual data (e.g., a savanna) with factual knowledge (e.g., lions). Another benchmark, WebQA, uses text and images to evaluate cross-modal retrieval, where a system must find relevant images or text snippets based on queries in another modality. The COCO Captions and Flickr30k datasets are also used to test image-text alignment, measuring how well models match descriptions to visuals. These benchmarks often use metrics like recall@k (how often correct results appear in the top-k retrieved items) or multimodal similarity scores.
For RAG, benchmarks like Natural Questions (NQ) and HotpotQA focus on retrieving and synthesizing information from text documents. While these are text-only, extensions like MultiModalQA combine tables, text, and images to test RAG systems that handle mixed data types. For instance, answering “What is the population of the city shown in this chart?” requires extracting data from a table, understanding a chart image, and generating a coherent answer. Metrics for RAG include answer accuracy, retrieval precision (percentage of relevant documents fetched), and generation quality (e.g., BLEU or ROUGE scores for text outputs). Challenges like KILT (Knowledge-Intensive Language Tasks) unify tasks like fact-checking and dialogue to test end-to-end RAG performance across domains.
Developers should also consider practical factors when choosing benchmarks. For multimodal search, latency and scalability matter when handling large datasets of images or videos. For RAG, balancing retrieval breadth (covering diverse sources) with generation coherence is critical. Tools like BEIR (Benchmarking IR Systems) provide modular frameworks to evaluate retrieval components, which can be adapted for multimodal use cases. While existing benchmarks are useful, many real-world applications require custom evaluations to address domain-specific needs, such as medical imaging with diagnostic reports or e-commerce product searches combining text and visuals.