Model Understanding

Model understanding focuses on interpreting the internal workings and decision-making processes of complex machine learning models, particularly large language and vision-language models, to improve their reliability and trustworthiness. Current research emphasizes developing rigorous benchmarks and evaluation frameworks to assess model comprehension, including tests of reasoning abilities, handling of ambiguous queries, and sensitivity to data perturbations. This work is crucial for identifying and mitigating biases, improving model robustness, and fostering greater transparency and explainability in AI systems, ultimately leading to safer and more effective applications across various domains.