Summary of Mitigating Metric Bias in Minimum Bayes Risk Decoding, by Geza Kovacs et al.
Mitigating Metric Bias in Minimum Bayes Risk Decoding
by Geza Kovacs, Daniel Deutsch, Markus Freitag
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to addressing the “metric bias” challenge in Minimum Bayes Risk (MBR) decoding is proposed. Traditional methods like greedy or beam search are outperformed by MBR decoding using metrics such as COMET or MetricX, but this comes with the issue of introducing metric bias. This bias occurs when the same metric used for decoding also serves as the evaluation metric, allowing “reward hacking” rather than genuine quality improvements. To mitigate this issue, an ensemble of utility metrics is used during MBR decoding, demonstrating improved performance over a single utility metric. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to fix a problem in language translation is being explored. Right now, a method called Minimum Bayes Risk (MBR) is the best way to translate text from one language to another. But this method has a big issue: it’s hard to tell if the translations are really good or just seem good because of how we measure them. To solve this problem, researchers are trying out a new approach that uses multiple ways to measure translation quality at the same time. This helps make sure that the translations are actually good and not just seeming good because of how they’re measured. |
Keywords
» Artificial intelligence » Translation
