Summary of Hyperparameter-free Approach For Faster Minimum Bayes Risk Decoding, by Yuu Jinnai and Kaito Ariu
Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
by Yuu Jinnai, Kaito Ariu
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 abstract presents a new approach to decoding in text generation tasks called Approximate Minimum Bayes-Risk (AMBR). This method aims to reduce the computational cost of traditional Minimum Bayes-Risk (MBR) decoding, which is often impractical due to its time-consuming inference process. AMBR is derived from the medoid identification problem and uses the Correlated Sequential Halving (CSH) algorithm to approximate the MBR objective. The authors evaluate AMBR on machine translation, text summarization, and image captioning tasks, showing that it achieves comparable results to Confidence-based Pruning (CBP), which requires hyperparameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AMBR is a new way to make computers generate text more efficiently. Right now, a method called MBR takes too long to figure out the best words to use. AMBR tries to solve this problem by finding the closest match in a special kind of math problem. It uses an algorithm called CSH to do this quickly and accurately. The authors tested AMBR on different tasks like translating languages, summarizing text, and creating captions for images. They found that AMBR worked just as well as another method called CBP, but without needing to adjust any settings. |
Keywords
» Artificial intelligence » Hyperparameter » Image captioning » Inference » Pruning » Summarization » Text generation » Translation
