If your target domain is scientific papers and Wikipedi...

To adapt to a scientific domain, train the KenLM on a curated seed set of papers from sources like arXiv or PubMed. If the seed set is small (under 1B tokens), use Kneser-Ney backoff to improve robustness by utilizing lower-order n-grams. Alternatively, implement an importance-sampling estimator that explicitly models both the target and raw data distributions, which provides more principled weighting than a hard threshold.