There is a lack of an evaluation methodology that estimates the extent to which large language models (LLMs) use code-switching (CS) in the same way as bilinguals.Existing methods do not have wide language coverage, fail to account for the diverse range of CS phenomena, or do not scale.We propose an intervention based on minimal pairs of CS.Each minimal pair contains one naturally occurring CS sentence and one minimally manipulated variant.We collect up to 1,000 such pairs each for 11 language pairs.Our human experiments show that, for every language pair, bilinguals consistently prefer the naturally occurring CS sentence.Meanwhile our experiments with current LLMs show that the larger the model, the more consistently it assigns higher probability to the naturally occurring CS sentence than to the variant.In accordance with theoretical claims, the largest probability differences arise in those pairs where the manipulated material consisted of closed-class words. 1

Segmenting text into sentences plays an early and crucial role in many NLP systems.This is commonly achieved by using rule-based or statistical methods relying on lexical features such as punctuation.Although some recent works no longer exclusively rely on punctuation, we find that no prior method achieves all of (i) robustness to missing punctuation, (ii) effective adaptability to new domains, and (iii) high efficiency.We introduce a new model -Segment any Text (SAT) -to solve this problem.To enhance robustness, we propose a new pretraining scheme that ensures less reliance on punctuation.To address adaptability, we introduce an extra stage of parameter-efficient fine-tuning, establishing state-of-the-art performance in distinct domains such as verses from lyrics and legal documents.Along the way, we introduce architectural modifications that result in a threefold gain in speed over the previous state of the art and solve spurious reliance on context far in the future.Finally, we introduce a variant of our model with fine-tuning on a diverse, multilingual mixture of sentence-segmented data, acting as a drop-in replacement and enhancement for existing segmentation tools.Overall, our contributions provide a universal approach for segmenting any text.Our method outperforms all baselines -including strong LLMs -across 8 corpora spanning diverse domains and languages, especially in practically relevant situations where text is poorly formatted.1