Code-switching is a common practice for millions of multilingual speakers but remains challenging for Large Language Models (LLMs). This paper investigates LLM capabilities in generating code-switched text, conducting extensive experiments across five diverse language pairs: English paired with Hindi, Tamil, Malayalam, and Indonesian, as well as Indonesian-Javanese. Our analysis, grounded in comprehensive human evaluations by native speakers, uncovers a directional asymmetry: LLMs consistently produce higher-quality (more accurate and fluent) code-switched text when prompted with a lower-resource language (e.g., Hindi, Tamil, Javanese) as the source, compared to when a higher-resource language (English, Indonesian) serves as the source. This asymmetry mirrors sociolinguistic patterns, particularly the Matrix Language Frame model, suggesting LLMs implicitly learn common code-switching structures from their training data where regional languages often form the grammatical base. Furthermore, we find that explicit linguistic guidance, applied through Equivalence Constraint Theory (ECT) to identify switching points, primarily benefits generation quality only in the less common, higher-resource-source direction where LLMs intrinsically struggle. These findings highlight a crucial interplay between the implicit linguistic knowledge captured by LLMs and the targeted utility of explicit linguistic constraints. We also introduce CSPref, a pairwise preference dataset derived from our human evaluations, to facilitate future research in code-switching generation and evaluation.

Existing research on Tabular Natural Language Inference (TNLI) exclusively examines the task in a monolingual setting where the tabular premise and hypothesis are in the same language. However, due to the uneven distribution of text resources on the web across languages, it is common to have the tabular premise in a high resource language and the hypothesis in a low resource language. As a result, we present the challenging task of bilingual Tabular Natural Language Inference (bTNLI), in which the tabular premise and a hypothesis over it are in two separate languages. We construct EI-InfoTabS: an English-Indic bTNLI dataset by translating the textual hypotheses of the English TNLI dataset InfoTabS into eleven major Indian languages. We thoroughly investigate how pre-trained multilingual models learn and perform on EI-InfoTabS. Our study shows that the performance on bTNLI can be close to its monolingual counterpart, with translate-train, translate-test and unified-train being strongly competitive baselines.