Disentangling Language Understanding and Reasoning Structures in Cross-lingual Chain-of-Thought Prompting (2025)

Abstract: Cross-lingual chain-of-thought prompting techniques have proven effective for investigating diverse reasoning paths in Large Language Models (LLMs), especially for low-resource languages. Despite these empirical gains, the mechanisms underlying cross-lingual improvements remain perplexing. This study, therefore, addresses whether the benefits of cross-lingual prompting arise from language-specific reasoning structures intrinsic to each language, or are simply a consequence of improved comprehension through cross-linguistic exposure. We employ neuron intervention and perturbation techniques to analyze and deactivate language-specific reasoning neurons during cross-lingual prompting, leading to performance disparities across languages, up to 27.4%. Our findings disentangle that these neurons are essential for reasoning in their respective languages, but have minimal effect on reasoning in other languages, providing evidence for the existence of language-specific local reasoning structures and guiding the development of more interpretable and effective multilingual AI systems.

## introduction large language models (llms) have demonstrated remarkable reasoning capabilities, yet performance disparities across languages persist, particularly for low-resource languages with limited training data @xcite . chain-of-thought (cot) prompting @xcite , which guides models to break problems into sequential reasoning steps, has become a go-to strategy to unlock latent reasoning power, potentially due to the ability of cot to leverage local clusters of "reasoning locality" @xcite , and has been effectively applied as cross-lingual prompting in parameter-frozen multilingual llms (mllms) @xcite . the significant performance improvements observed with cross-lingual prompting, particularly for low-resource languages, are welldocumented, but the underlying mechanisms driving these gains are still not fully understood. a fundamental question remains entangled: do improvements from cross-lingual prompting arise from distinct local reasoning structures intrinsic to each language, or are they simply by-products of enhanced problem comprehension achieved through cross-linguistic exposure? figure 1 illustrates this vital distinction. when presented with identical mathematical problems in different languages, llms often produce different reasoning processes and solutions. the source is unclear, whether certain languages inherently scaffold different reasoning structures through their syntax, morphology, and discourse conventions, or framing the same problem in multiple languages simply clarifies ambiguities and better translations. to address this question, our work offers a comprehensive analysis aimed at disentangling whether cross-lingual prompting yields genuinely distinct local reasoning structures per language or merely enhances the understanding of problems originally written in low-resource languages. our main contributions are: ## methodology we investigate the origins of multilingual reasoning in llms by analyzing the internal activation patterns and functional roles of language-specific and shared neural pathways during cross-lingual prompting for mathematical and commonsense reasoning tasks. the experimental design comprises multiple phases, each aimed at uncovering the structure of reasoning processes across languages. the first phase identifies neurons that are highly specific to reasoning in individual languages. leveraging the lape technique @xcite , which operates by calculating the entropy of each feed-forward network neuron's activation probabil-ities across large text corpora from multiple languages, neurons exhibiting low entropy, meaning they preferentially activate for a single language, are considered language-specific. next, to evaluate how the model encodes linguistic information, multilingual queries are fed into the model, and output embeddings at each layer are decoded into model vocabulary tokens. these decoded tokens are classified as either english or non-english. results reveal that, in the initial layers, embeddings produced from non-english queries predominantly correspond to non-english tokens while in intermediate layers, english tokens begin to take over. this pattern suggests that the model initially processes and comprehends non-english inputs through language-specific pathways @xcite , therefore, deactivation of language neurons in intermediate layers does not impact the model's understanding of the input question, indicating them as language-specific reasoning neurons. to provide a robust baseline for performance analysis, we employ cross-lingual tree-of-thought (cross-tot) prompting @xcite and perform intervention with languagespecific reasoning neurons, as shown in figure 2 . in this setup, the model is prompted to simulate collaboration among mathematicians/experts, each working in their native language. the original prompt encourages each one to reason step by step in their language, refining and cross-referencing their solutions at each stage until a consensus is reached. here, we refine the prompt to explicitly label each reasoning path according to the expert's language, allowing for extraction and analysis of the reasoning trajectory for each language. key performance metrics, including logical soundness and correctness, are evaluated using llm-as-a-judge framework @xcite . the final phase examines the results and behaviors of neuron deactivation interventions. the impact on performance is assessed by measuring changes in the metrics after selectively disabling language-specific neurons. by analyzing which reasoning paths and performance metrics are most affected by these perturbations, we analyze whether distinct local reasoning structures are maintained for each language or if reasoning converges on a shared representational core. for instance, if deactivating neurons specific to language x disrupts reasoning only for that language while leaving others unaffected, it provides strong evidence for the existence of language-specific reasoning circuits. ## conclusion we demonstrate that llms exhibit distinct language-specific reasoning and language understanding structures, with specialized local reasoning neurons that primarily support inference within individual languages. by systematically analyzing the effects of targeted neuron deactivation, we find that these language-specific neurons are crucial for reasoning in their respective languages, while having limited impact on reasoning in others. these results provide strong evidence that multilingual llms do not rely solely on a shared reasoning core but instead develop local reasoning structures for different languages. our findings offer valuable insight into the internal organization of cross-lingual reasoning and new directions for developing more interpretable and effective multilingual ai systems.