Shared Circuits for Shared Grammar: Tracing Subject-Verb Agreement Across Languages
- URL: http://arxiv.org/abs/2608.18545v1
- Date: Wed, 19 Aug 2026 05:05:15 GMT
- Title: Shared Circuits for Shared Grammar: Tracing Subject-Verb Agreement Across Languages
- Abstract summary: We find that languages with overt person/number inflection exhibit more similar agreement circuitry than non-conjugating languages.<n>Many implicated heads display similar attention patterns across languages, suggesting that cross-lingual overlap reflects shared functional roles as well as shared localization.
- Score: 18.33287987422947
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multilingual large language models often generalize across languages, and prior work suggests that their internal mechanisms can overlap cross-lingually. It remains unclear, however, when such sharing emerges and whether it varies with the overt realization of the same grammatical operation. We investigate this question for present-tense subject-verb agreement, a morphosyntactic process that varies substantially across languages and is only weakly expressed in English. Using activation patching and attention analysis across 29 languages and five open-source model families, we identify the attention heads causally implicated in agreement and compare these head-level signatures across languages. We find that languages with overt person/number inflection exhibit more similar agreement circuitry than non-conjugating languages, with the strongest sharing appearing when the analysis isolates recovery of the inflectional contrast itself. English provides an informative bridge case, becoming more similar to conjugating languages precisely in contexts where overt agreement is required. Finally, many implicated heads display similar attention patterns across languages, suggesting that cross-lingual overlap reflects shared functional roles as well as shared localization. Together, these results indicate that multilingual LLMs reuse partially shared computational structure for morphosyntactic agreement rather than relying on fully separate language-specific solutions.
Related papers
- Tokenizing Crosslingual Homographs [8.254230288283258]
Multilingual language models rely on shared subword vocabularies to represent multiple languages within a limited number of token units.<n>We investigate this limitation through cross-lingual homographs and false friends.<n>We propose a simple tokenizer-level intervention based on language cues: language-specific characters replacing initial characters of shared-vocabulary words.
arXiv Detail & Related papers (2026-07-20T08:37:31Z) - When Meanings Meet: Investigating the Emergence and Quality of Shared Concept Spaces during Multilingual Language Model Training [57.230355403478995]
We investigate the development of language-agnostic concept spaces during pretraining of EuroLLM.<n>We find that shared concept spaces emerge early and continue to refine, but that alignment with them is language-dependent.<n>In contrast to prior work, our fine-grained manual analysis reveals that some apparent gains in translation quality reflect shifts in behavior.
arXiv Detail & Related papers (2026-01-30T11:23:01Z) - On the Entity-Level Alignment in Crosslingual Consistency [62.33186691736433]
SubSub and SubInj integrate English translations of subjects into prompts across languages, leading to substantial gains in factual recall accuracy and consistency.<n>These interventions reinforce the entity representation alignment in the conceptual space through model's internal pivot-language processing.
arXiv Detail & Related papers (2025-10-11T16:26:50Z) - False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models [53.01170039144264]
Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages.<n>Does token overlap facilitate cross-lingual transfer or instead introduce interference between languages?<n>We find that models with overlap outperform models with disjoint vocabularies.
arXiv Detail & Related papers (2025-09-23T07:47:54Z) - High-Dimensional Interlingual Representations of Large Language Models [65.77317753001954]
Large language models (LLMs) trained on massive multilingual datasets hint at the formation of interlingual constructs.<n>We explore 31 diverse languages varying on their resource-levels, typologies, and geographical regions.<n>We find that multilingual LLMs exhibit inconsistent cross-lingual alignments.
arXiv Detail & Related papers (2025-03-14T10:39:27Z) - Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages [15.203789021094982]
In large language models (LLMs), how are multiple languages learned and encoded?<n>We train sparse autoencoders on Llama-3-8B and Aya-23-8B, and demonstrate that abstract grammatical concepts are often encoded in feature directions shared across many languages.
arXiv Detail & Related papers (2025-01-10T21:18:21Z) - When is BERT Multilingual? Isolating Crucial Ingredients for
Cross-lingual Transfer [15.578267998149743]
We show that the absence of sub-word overlap significantly affects zero-shot transfer when languages differ in their word order.
There is a strong correlation between transfer performance and word embedding alignment between languages.
Our results call for focus in multilingual models on explicitly improving word embedding alignment between languages.
arXiv Detail & Related papers (2021-10-27T21:25:39Z) - Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
We generate language representation from multilingual pre-trained models and conduct linguistic analysis.
We cluster all the target languages into multiple groups and name each group as a representation sprachbund.
Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
arXiv Detail & Related papers (2021-09-01T09:32:06Z) - Bridging Linguistic Typology and Multilingual Machine Translation with
Multi-View Language Representations [83.27475281544868]
We use singular vector canonical correlation analysis to study what kind of information is induced from each source.
We observe that our representations embed typology and strengthen correlations with language relationships.
We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
arXiv Detail & Related papers (2020-04-30T16:25:39Z)
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.