Are the LLMs Capable of Maintaining at Least the Language Genus?
Not on the map. Why?
Abstract (arXiv)
Large Language Models (LLMs) display notable variation in multilingual behavior, yet the role of genealogical language structure in shaping this variation remains underexplored. In this paper, we investigate whether LLMs exhibit sensitivity to linguistic genera by extending prior analyses on the MultiQ dataset. We first check if models prefer to switch to genealogically related languages when prompt language fidelity is not maintained. Next, we investigate whether knowledge consistency is better preserved within than across genera. We show that genus-level effects are present but strongly conditioned by training resource availability. We further observe distinct multilingual strategies across LLMs families. Our findings suggest that LLMs encode aspects of genus-level structure, but training data imbalances remain the primary factor shaping their multilingual performance.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 24 Oct 2025.
Builds on
16 of 24 references.
References · 16
- 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
- 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
- 2023The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variantscited by 311
- 2024Multilingual Instruction Tuning With Just a Pinch of Multilingualitycited by 73
- 2024Question Translation Training for Better Multilingual Reasoningcited by 42
- 2024Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?cited by 41
- 2024Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQcited by 27
- 2025DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluationcited by 21
- 2024Self-Reflection Makes Large Language Models Safer, Less Biased, and Ideologically Neutralcited by 20
- 2023Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruningcited by 17
- 2024SambaLingo: Teaching Large Language Models New Languagescited by 11
- 2025Multilingual Performance Biases of Large Language Models in Educationcited by 9
4 more
- 2024Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysiscited by 7
- 2025Alignment Drift in CEFR-prompted LLMs for Interactive Spanish Tutoringcited by 7
- 2025Reasoning Transfer for an Extremely Low-Resource and Endangered Language: Bridging Languages Through Sample-Efficient Language Understandingcited by 5
- 2025Exploring the Feasibility of Multilingual Grammatical Error Correction with a Single LLM up to 9B parameters: A Comparative Study of 17 Modelscited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.