Summary of Ceret: Cost-effective Extrinsic Refinement For Text Generation, by Jason Cai et al.
CERET: Cost-Effective Extrinsic Refinement for Text Generation
by Jason Cai, Hang Su, Monica Sunkara, Igor Shalyminov, Saab Mansour
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed CERET method refines text generations by incorporating semantic stability, entailment, and inter-sample uncertainty measures. This approach outperforms existing self-improvement methods, such as Self-consistency and Self-rerank, in tasks like abstractive summarization and question answering, with significant improvements in Rouge-1 (1.6%) and hit rate (3.5%). The CERET method requires 9.4% of the latency compared to LLM Self-rerank, making it a more cost-effective solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CERET is a new way to make text generation better. It does this by thinking about how stable the generated text is, and whether it makes sense with what came before. This helps it generate higher-quality text that’s closer to what humans would write. In tests, CERET did much better than other methods at things like summarizing texts and answering questions. Plus, it was faster and used less computing power. |
Keywords
» Artificial intelligence » Question answering » Rouge » Summarization » Text generation
