Summary of Mixeval-x: Any-to-any Evaluations From Real-world Data Mixtures, by Jinjie Ni et al.
MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
by Jinjie Ni, Yifan Song, Deepanway Ghosal, Bo Li, David Junhao Zhang, Xiang Yue, Fuzhao Xue, Zian Zheng, Kaichen Zhang, Mahir Shah, Kabir Jain, Yang You, Michael Shieh
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 In this paper, researchers identify two major issues with current evaluations for AI models: inconsistent standards across different communities and significant biases in query, grading, and generalization. To address these problems, they introduce MixEval-X, a real-world benchmark that optimizes and standardizes evaluations across diverse input and output modalities. The authors propose a multi-modal benchmark mixture and adaptation-rectification pipelines to reconstruct real-world task distributions, ensuring that evaluations generalize effectively to real-world use cases. They demonstrate the effectiveness of their approach through extensive meta-evaluations, showing strong correlations with crowd-sourced real-world evaluations (up to 0.98). This paper provides comprehensive leaderboards for reranking existing models and organizations, as well as insights to enhance understanding of multi-modal evaluations and inform future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers are working on a new way to test how well AI models can understand and generate different types of data. Right now, there’s no standard way to do this, which makes it hard to compare the performance of different models. The authors of this paper think that if they create a benchmark that includes many different types of data, it will help them figure out what works best. They call their new benchmark MixEval-X and show that it can be used to test how well AI models do in real-world situations. |
Keywords
» Artificial intelligence » Generalization » Multi modal
