Summary of Sample Then Identify: a General Framework For Risk Control and Assessment in Multimodal Large Language Models, by Qingni Wang et al.
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models
by Qingni Wang, Tiantian Geng, Zhiyuan Wang, Teng Wang, Bo Fu, Feng Zheng
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 The paper proposes a new framework called TRON for risk control and assessment in Multimodal Large Language Models (MLLMs). Specifically, it introduces a two-step approach that allows for sampling response sets of minimum size and identifying high-quality responses based on self-consistency theory. The framework is applicable to any MLLM supporting sampling in both open-ended and closed-ended scenarios. The authors also investigate semantic redundancy in prediction sets within open-ended contexts, leading to a new evaluation metric for MLLMs. The paper presents comprehensive experiments across four Video Question-Answering (VideoQA) datasets utilizing eight MLLMs, demonstrating the effectiveness of TRON in achieving desired error rates bounded by two user-specified risk levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRON is a new way to make sure that Multimodal Large Language Models (MLLMs) are trustworthy. These models can be used for many tasks, but they often don’t know when they’re making mistakes. The paper introduces a special kind of model called TRON that helps MLLMs make better decisions by sampling responses and finding the best ones. This is important because it allows users to control how much risk they’re willing to take when using these models. The researchers tested TRON on four different datasets and showed that it works well in making sure the models don’t make too many mistakes. |
Keywords
» Artificial intelligence » Question answering
