CLBench-V: A Benchmark for Multimodal Context Learning
A new benchmark called CLBench-V has been developed by researchers to assess multimodal context learning in artificial intelligence models. This evaluation differs from existing ones by incorporating real-world situations that involve multiple types of context, including scientific figures, financial documents, maps, and web pages. CLBench-V categorizes tasks into three key areas: grounding context, applying new information, and acquiring new knowledge. It merges adapted public benchmarks with freshly created datasets covering science, finance, long-document comprehension, and spatial reasoning. This research is documented in a paper available on arXiv (2607.25294v1).
Key facts
- CLBench-V is a benchmark for multimodal context learning.
- It addresses the difficulty of localizing where context use breaks down.
- Tasks are organized around three dimensions: context grounding, new information application, and new knowledge learning.
- It combines converted public benchmarks with newly constructed datasets.
- Domains include science, finance, long-document understanding, and spatial reasoning.
- The paper is available on arXiv with ID 2607.25294v1.
- Existing evaluations mainly focus on textual contexts.
- Real-world tasks often require learning from multimodal context.
Entities
Institutions
- arXiv