Summary of Unveiling Uncertainty: a Deep Dive Into Calibration and Performance Of Multimodal Large Language Models, by Zijun Chen et al.
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
by Zijun Chen, Wenbo Hu, Guande He, Zhijie Deng, Zheng Zhang, Richang Hong
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A multimodal large language model (MLLM) combines visual and textual data to perform tasks like image captioning and visual question answering. However, accurate uncertainty calibration is crucial for reliable use in areas such as healthcare and autonomous driving. This paper investigates representative MLLMs’ calibration across various scenarios, including before and after visual fine-tuning, as well as before and after multimodal training of the base LLMs. The results show miscalibration in their performance, but no significant differences in calibration across these scenarios. The study also highlights how uncertainty differs between text and images and how their integration affects overall uncertainty. The paper constructs an IDK (I don’t know) dataset to evaluate MLLMs’ ability to handle unknowns and self-assess uncertainty. The findings reveal that MLLMs tend to provide answers rather than admit uncertainty, but this self-assessment improves with proper prompt adjustments. To calibrate MLLMs and enhance model reliability, the paper proposes techniques like temperature scaling and iterative prompt optimization. The results provide insights into improving MLLMs for effective and responsible deployment in multimodal applications. The proposed techniques can be used to improve the performance of MLLMs in various tasks, including image captioning and visual question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) combine text and images to perform tasks like captioning pictures and answering questions about what’s in them. These models are important for things like self-driving cars and helping doctors diagnose diseases. But they need to be able to say when they’re not sure, or they might make mistakes. The authors of this paper looked at how well some of these LLMs do at saying when they’re not sure. They found that the LLMs are often too confident and don’t admit when they’re unsure. This is a problem because it can lead to mistakes. To fix this, the authors came up with new ways to make the LLMs more accurate. These methods help the models say when they’re not sure and also improve their overall performance. The results of this study can be used to make these models better for things like self-driving cars and medical diagnosis. |
Keywords
» Artificial intelligence » Fine tuning » Image captioning » Large language model » Optimization » Prompt » Question answering » Temperature
