Computer Science > Computation and Language
arXiv:2409.09613 (cs)
[Submitted on 15 Sep 2024]
Title:Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
View a PDF of the paper titled Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora, by Yungi Kim and 5 other authors
View PDFHTML (experimental)Abstract:With the increasing demand for substantial amounts of high-quality data to train large language models (LLMs), efficiently filtering large web corpora has become a critical challenge. For this purpose, KenLM, a lightweight n-gram-based language model that operates on CPUs, is widely used. However, the traditional method of training KenLM utilizes only high-quality data and, consequently, does not explicitly learn the linguistic patterns of low-quality data. To address this issue, we propose an ensemble approach that leverages two contrasting KenLMs: (i) Good KenLM, trained on high-quality data; and (ii) Bad KenLM, trained on low-quality data. Experimental results demonstrate that our approach significantly reduces noisy content while preserving high-quality content compared to the traditional KenLM training method. This indicates that our method can be a practical solution with minimal computational overhead for resource-constrained environments.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2409.09613 [cs.CL] |
| (orarXiv:2409.09613v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2409.09613 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora, by Yungi Kim and 5 other authors
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