Summary of Generating Diverse and High-quality Texts by Minimum Bayes Risk Decoding, By Yuu Jinnai et al.
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
by Yuu Jinnai, Ukyo Honda, Tetsuro Morimura, Peinan Zhang
First submitted to arxiv on: 10 Jan 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 Medium Difficulty summary: This paper proposes two novel decoding algorithms, Diverse MBR (DMBR) and k-medoids MBR (KMBR), which leverage Minimum Bayes-Risk (MBR) decoding to generate sentences that are both correct and diverse. Unlike existing methods relying on beam search or random sampling, the proposed approach integrates diversity objectives into MBR decoding, enabling the generation of high-quality and diverse outputs. The authors evaluate their method on various directed text generation tasks using encoder-decoder models and a large language model with prompting, demonstrating a better trade-off compared to diverse beam search and sampling algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making computers generate text that is both correct and different from each other. Right now, most computer programs can only generate one type of text, like news articles or chatbot responses. But humans are able to write many different types of texts, like stories, poems, and emails. The authors want to create a new way for computers to generate text that is more like what humans do. They propose two new methods that combine an existing technique called Minimum Bayes-Risk decoding with ideas about diversity. This allows the computer program to generate multiple sentences that are not only accurate but also different from each other. |
Keywords
» Artificial intelligence » Encoder decoder » Large language model » Prompting » Text generation
