Summary of Metametrics: Calibrating Metrics For Generation Tasks Using Human Preferences, by Genta Indra Winata et al.
MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferences
by Genta Indra Winata, David Anugraha, Lucky Susanto, Garry Kuwanto, Derry Tanti Wijaya
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 As machine learning educators, we need to evaluate model outputs effectively to align with human preferences. However, existing performance metrics often excel in one area but not all dimensions. To address this issue, we introduce MetaMetrics, a calibrated meta-metric that optimizes the combination of existing metrics for generation tasks across different modalities in a supervised manner. Our metric demonstrates flexibility and effectiveness in language and vision downstream tasks, showing significant benefits across various multilingual and multi-domain scenarios. This makes MetaMetrics a powerful tool for improving the evaluation of generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MetaMetrics is a new way to measure how well models perform when creating text or images that humans like. Right now, we have many different ways to evaluate these models, but they’re not all perfect. Some are good at measuring one thing, but not others. MetaMetrics fixes this by combining the best parts of each evaluation method into one score. It works really well in lots of different situations and is easy to use. |
Keywords
» Artificial intelligence » Machine learning » Supervised
