XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models

1. 1Design a benchmark to test cross-modal consistency
2. 2Create a large-scale tri-modal dataset with audio, vision, and text modalities
3. 3Evaluate omni-language models on the benchmark to identify modality-invariant reasoning and modality-specific biases
4. 4Analyze results to improve model performance and consistency

AI researchers and engineers working on omni-language models can benefit from XModBench to evaluate and improve their models' cross-modal consistency, while product managers can use it to inform decisions about model deployment

💡 XModBench helps evaluate whether omni-language models achieve modality-invariant reasoning or exhibit modality-specific biases

🤖 Introducing XModBench: a benchmark for evaluating cross-modal capabilities in omni-language models 📊