Are the LLMs Capable of Maintaining at Least the Language Genus?
- URL: http://arxiv.org/abs/2510.21561v1
- Date: Fri, 24 Oct 2025 15:20:40 GMT
- Title: Are the LLMs Capable of Maintaining at Least the Language Genus?
- Authors: Sandra Mitrović, David Kletz, Ljiljana Dolamic, Fabio Rinaldi,
- Abstract summary: We show that genus-level effects are present but strongly conditioned by training resource availability.<n>Our findings suggest that LLMs encode aspects of genus-level structure, but training data imbalances remain the primary factor shaping their multilingual performance.
- Score: 5.748049484273442
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders [51.380449540006985]
Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear.<n>Do they form shared multilingual representations with language-specific decoding, and if so, why does performance still favor the dominant training language?<n>We analyze their internal mechanisms using cross-layer transcoders (CLT) and attribution graphs.
arXiv Detail & Related papers (2025-11-13T22:51:06Z) - The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
arXiv Detail & Related papers (2025-06-11T16:00:54Z) - When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners [111.50503126693444]
We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
arXiv Detail & Related papers (2025-05-21T08:35:05Z) - Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning [9.75748930802634]
We study the power of in-context-learning to improve machine learning performance.<n>We consider an extremely simple domain: next token prediction on simple language tasks.<n>We find that LLMs uniformly underperform n-gram models on this task.
arXiv Detail & Related papers (2025-01-06T07:57:51Z) - The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model [59.357993924917]
We study the evolution of multilingual capabilities in large language models (LLMs) during the pre-training process.<n>We propose the Babel Tower Hypothesis, which describes the entire process of LLMs acquiring new language capabilities.<n>We propose a novel method to construct an optimized pre-training corpus for multilingual code LLMs.
arXiv Detail & Related papers (2024-12-10T08:28:57Z) - Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models [11.423589362950812]
Large language models (LLMs) have demonstrated remarkable performance, particularly in multilingual contexts.<n>Recent studies suggest that LLMs can transfer skills learned in one language to others, but the internal mechanisms behind this ability remain unclear.<n>This paper provides insights into the internal workings of LLMs, offering a foundation for future improvements in their cross-lingual capabilities.
arXiv Detail & Related papers (2024-10-15T15:49:15Z) - Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models [22.859955360764275]
We introduce the MultiLingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information.
We evaluate four state-of-the-art large language models on MLNeedle.
arXiv Detail & Related papers (2024-08-19T17:02:06Z) - Faux Polyglot: A Study on Information Disparity in Multilingual Large Language Models [7.615938028813914]
We studied linguistic preference in a cross-language RAG-based information search setting.<n>We found that LLMs displayed systemic bias towards information in the same language as the query language.
arXiv Detail & Related papers (2024-07-07T21:26:36Z) - Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
Large Language Models (LLMs) have shown impressive language capabilities.
In this work, we investigate the spontaneous multilingual alignment improvement of LLMs.
We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
arXiv Detail & Related papers (2024-05-22T16:46:19Z) - Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.
Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
arXiv Detail & Related papers (2024-02-26T09:36:05Z) - How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering [52.86931192259096]
Knowledge Base Question Answering (KBQA) aims to answer natural language questions based on facts in knowledge bases.
Recent works leverage the capabilities of large language models (LLMs) for logical form generation to improve performance.
arXiv Detail & Related papers (2024-01-11T09:27:50Z) - Don't Trust ChatGPT when Your Question is not in English: A Study of
Multilingual Abilities and Types of LLMs [16.770697902481107]
Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities.
We propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings.
The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
arXiv Detail & Related papers (2023-05-24T02:05:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.