Evaluating Multimodal Faithfulness
Mohit discusses the importance of incorporating causality and explainability in model evaluation, emphasizing that simply getting the right answer isn't sufficient. He explores the complexities of assessing factuality in multimodal contexts, noting that while multiple modalities can enhance accuracy, they also introduce new challenges. The conversation highlights the dual role of metrics in both evaluation and supervision to reduce hallucinations in multimodal summary generation.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Unifying Vision and Language Models with Mohit Bansal - 636
Related Questions
What metrics are important in evaluating artificial intelligence, specifically in the context of the episode Unifying Vision and Language Models with Mohit Bansal - 636 and the clip Evaluating Generative Models?
What are the limitations of the vision model in multimodal systems as discussed in the episode Google’s Multimodal Med-PaLM with Vivek Natarajan and Tao Tu and the clip AI Scaling Insights?
What are the limitations of the vision model in multimodal systems as discussed in the episode Google’s Multimodal Med-PaLM with Vivek Natarajan and Tao Tu and the clip AI Scaling Insights?