eneoli-cost-action-bc3c3012·1 events·first seen Aliases: ENEOLI COST Action
Researchers introduce LexNeo-Bench, a 3,050-instance benchmark for evaluating LLM performance on lexical borrowing classification and neology detection in Luxembourgish, a low-resource contact language. Three multilingual LLMs are tested across 34 prompt configurations; without external context, models perform near chance on borrowing classification (25–35%). Injecting instance-specific subgraphs from a linguistic knowledge graph raises accuracy to 71–81% and largely closes the gap between small and large models, though neology detection remains difficult. The study highlights the value of lexicon-aware, structured prompting for low-resource multilingual evaluation.