Summary of Rethinking Kenlm: Good and Bad Model Ensembles For Efficient Text Quality Filtering in Large Web Corpora, by Yungi Kim et al.
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
by Yungi Kim, Hyunsoo Ha, Sukyung Lee, Jihoo Kim, Seonghoon Yang, Chanjun Park
First submitted to arxiv on: 15 Sep 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 The proposed ensemble approach leverages two contrasting KenLMs, one trained on high-quality data and another on low-quality data, to efficiently filter large web corpora. By combining Good KenLM and Bad KenLM, the method significantly reduces noisy content while preserving high-quality content, making it a practical solution for resource-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative approach to train KenLM, a lightweight language model, by leveraging both good and bad data. This allows the model to learn from low-quality data, which is often ignored in traditional training methods. The result is a more robust and efficient method for filtering large web corpora, making it useful for resource-constrained environments. |
Keywords
» Artificial intelligence » Language model
