performance assessment
Performance assessment involves evaluating the effectiveness and accuracy of an AI model against predefined metrics. It encompasses various techniques including cross-validation, error analysis, and comparisons to baseline models.
- CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models
- FLiP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
- Face-Human-Bench: A Comprehensive Benchmark of Face and Human Understanding for Multi-modal Assistants
- Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence
- LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language
- MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
- Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab
- Steering When Necessary: Flexible Steering Large Language Models with Backtracking