Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages (2025)

Abstract: Human bilinguals often use similar brain regions to process multiple languages, depending on when they learned their second language and their proficiency. In large language models (LLMs), how are multiple languages learned and encoded? In this work, we explore the extent to which LLMs share representations of morphsyntactic concepts such as grammatical number, gender, and tense across languages. 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. We use causal interventions to verify the multilingual nature of these representations; specifically, we show that ablating only multilingual features decreases classifier performance to near-chance across languages. We then use these features to precisely modify model behavior in a machine translation task; this demonstrates both the generality and selectivity of these feature’s roles in the network. Our findings suggest that even models trained predominantly on English data can develop robust, cross-lingual abstractions of morphosyntactic concepts.

## introduction in the brains of human bilinguals, syntax processing may occur in similar regions for their first and second language, depending on factors like when the second language was learned @xcite , language proficiency @xcite , among many other factors @xcite . in multilingual language models (lms; @xcite , how apt is the analogy of shared processing to human bilinguals? if we desire parameter-efficiency, we might want multilingual representations of concepts such as grammatical number, rather than many language-specific repre-figure 1 : using sparse autoencoders, we find that language models share representations of grammatical concepts across languages. by intervening on these multilingual representations, we can change the model behavior given inputs in different languages. for example, we can make the model predict plural verbs in different languages by activating the same plural feature. past work has emphasized language-balanced pretraining corpora (e.g., @xcite @xcite , such that an lm could be said to have many primary languages. however, many of the best-performing multilingual lms are now primarily english models, trained on over 90% english text (e.g., @xcite . why do these models perform so well in non-english languages? we hypothesize that these models learn generalizable abstractions that enable more efficient learning of new languages. in other words, being able to deeply characterize a smaller distribution could allow models to acquire more robust abstractions that may generalize more effectively to a wider distribution post-hoc; in contrast, more balanced corpora (wider distributions) could encourage the model to start by learning language-specific abstractions, which are po-tentially never merged into higher-level languageinvariant concepts. for small models, on a synthetic language pair (english and a token-level duplication of it), @xcite show that imbalanced datasets lead to higher sample efficiency and higher overall performance in both languages in the lowdata regime. 1 in large language models, multilinguality has both practical and theoretical scientific value. high performance on natural language processing tasks across many languages increases the impact and inclusivity of language technologies, and in a more compute-efficient manner than would be possible by training a large series of monolingual models. moreover, multilingual models are hypothesized to be able to outperform monolingual models in low-resource languages: past work has emphasized the importance of cross-lingual transfer, where knowledge in one language is shared with another language whose corpora did not contain the information of interest @xcite @xcite . more abstractly, the extent to which abstract concepts are shared across languages addresses a key question: what kinds of multi-lingual processing mechanisms are acquirable from exposure to large-scale text distributions? it would be more parameter-efficient to learn generalizable abstractions that apply across languages, as opposed to redundantly learning the same grammatical feature for each language separately. moreover, the existence of generalizable abstractions informs debates on memorization versus generalization: cross-linguistic generalization suggests a more sophisticated and broad application of particular concepts. past work has largely investigated multilingual language models using behavioral/benchmarking analyses @xcite or neuron-level mechanistic analyses @xcite . @xcite observe that masked language models often share neurons across languages for a particular concept, but causally verifying this is difficult, as counterfactual interventions to neurons often affect the processing of irrelevant concepts (see §2). to overcome this challenge, we make our units of causal analysis sparse autoencoder features, which have been shown to be more monosemantic, and therefore more humaninterpretable @xcite @xcite . this enables more precise causal interventions @xcite , and allows us to qualitatively verify that features of interest are truly sensitive to the hypothesized feature, rather than a spurious but close-enough feature. in this work, we train a set of sparse autoencoders on the intermediate activations of llama-3-8b @xcite and aya-3-8b @xcite and locate (massively) multilingual features for various morphosyntactic concepts. we design experiments to quantify the degree to which these concepts are shared across languages, and validate their role in the models generations. our results reveal that language models share morphosyntactic concept representations across typologically diverse languages, and that the internal lingua franca of large language models may not be english words per se, but rather concepts. foot_2 2 background feature disentanglement using sparse autoencoders. the features underlying model computation are not guaranteed to be aligned to neuron bases; they may instead be represented in a distributed manner, such that there is a many-to-many relationship between neurons and concepts @xcite . in practice, single neurons foot_3 are often polysemantic; in other words, they activate on a range of seemingly unrelated concepts @xcite . for example, @xcite observe that a single neuron in a small lm responds to a mixture of academic citations, english dialogue, http requests, and korean text. to address this, @xcite ; @xcite propose sparse autoencoders (saes) as a scalable technique for unsupervised discovery of interpretable feature directions in neural networks. given an activation vector @xmath0, the autoencoder computes a decomposition a key idea underlying sae-based interpretability is that one can reinterpret the language model's (lm) internal computations in terms of the sae features. by applying the decomposition in eq. 1 to hidden states x in the lm and folding the saes into the forward pass, 4 we can express model representations as a combination of sae feature activations f i and reconstruction errors ϵ. this reframing enables gradient-based attribution patching techniques @xcite to identify which sae features have causal influence on particular aspects of model behavior. finding causally relevant features using attribution patching. causal interpretability methods aim to locate components in neural networks (such as attention heads or neurons) responsible for particular behaviors @xcite . this requires constructing a distribution d over pairs of inputs (x clean , x patch ), where x clean is a prompt on which the behaviour occurs, and x patch is a reference prompt on which the behaviour does not occur. then, we can quantify the importance of components using a target metric m : r @xmath1, e.g., the difference in log-probabilities between a correct token continuation and minimally differing incorrect token continuation. in other words, we quantify the importance of a component on the model behaviour as the expected change in l when replacing the components's activation on the clean prompt with its value on the patch prompt. this is known as the indirect effect (ie; @xcite . an exact but compute-intensive method for causal analysis is activation patching @xcite , where we observe the change in model behaviour when performing a counterfactual intervention to a component. however, this requires a separate forward pass for each component. therefore, 4 as described in detail in @xcite . here, m is the target metric, usually defined as the logit difference between a correct and minimally differing incorrect token completion given context x clean , a clean is the activation of component a given x clean , and x patch is a minimally differing context that changes the correct answer. thus, the intuition is that we can approximate the causal contribution of a to m by taking the slope of m with respect to a (where this is approximated as the gradient of m at a foot_4 ) and multiplying this by the change in activations in a. atp requires two forward passes and one backward pass to compute an estimate score for all components in parallel. in practice, we employ a more expensive but more accurate approximation based on integrated gradients @xcite . for details, please see app. c. probing classifiers. linear probes have frequently been used to locate representations of morphosyntactic features (e.g., @xcite @xcite . given labeled classification data, we may train logistic regressions to map from the activations of frozen intermediate layers of pre-trained models @xcite to the task labels. we similarly train classifiers to predict morphosyntactic concepts. we then quantify the extent of crosslingual feature sharing for morphosyntactic concepts, and later evaluate the selectivity of targeted morphosyntactic concept interventions. ## multilingual features in this section, we investigate the extent to which morphosyntactic concepts are shared (or redundantly encoded) across languages. we first measure the overlap between the most causally influential sparse features for a given concept across languages. then, we investigate whether ablating the multilingual features leads to a consistent decrease in classifier performance across languages. ## demonstrating functional selectivity: a case study in machine translation another benefit of sparse features is the level of fine-grained control they provide over language model behaviors @xcite . indeed, feature-based interventions tend to be more effective then neuron-based methods at precisely modifying how language models generalize without destroying task performance @xcite . we leverage this property in a machine translation setting to (i) demonstrate the functional selectivity of the discovered morphosyntactic features (i.e., to show that intervening with them has few side effects on other concepts), and (ii) provide additional evidence that their causal role is highly general (i.e., that it is effective in this mt setting and not just on the probes). to evaluate (i) and (ii), we use feature steering @xcite , where we clamp features of interest to counterfactual activations. these activations can be either significantly higher or lower than the values observed in practice. this approach is similar to prior work on understanding the functional role of individual neurons in image models @xcite . as translation is a difficult task that does not always have a single clear answer, we restrict to a smaller subsample of concepts and languages such that we can manually verify the baseline quality of translations, and then the efficacy of the interventions. we restrict our analysis to tense (past vs. present), gender (masc. vs. fem.), and number (sing. vs. plural), as these concepts are shared across most languages we consider. we analyze english, french, and german; we additionally include turkish, but restrict our focus to number and tense here, as turkish does not inflect for grammatical gender. we provide details on the prompt format and translation performance in appendix f. ## related work multilingual language modeling. multilingual language models are trained on multiple languages simultaneously, typically with an explicitly designed balance between languages in the pretraining corpus. for example, xlm-r @xcite , m2m @xcite , mt5 @xcite , and aya @xcite are trained with corpora balanced across many languages. in contrast, llama 3 (dubey et al., 2024) is trained on a much more english-centric distribution: over 90% of the corpus is english text. generally, the motivation behind balanced corpora during multilingual pretraining is to encourage cross-lingual transfer. we find that this balance may not be necessary for multilingual rep-turkish televizyon haberleri santralden yükselen beyaz dumanı gösteriyor. ## discussion and conclusion language models trained primarily on english data perform surprisingly well in other languages. prior work has emphasized the importance of balanced pretraining corpora, so as to not overfit to a sin-gle language @xcite ; however, more recent studies suggest that the size of the pretraining corpusnot balance-may be more important for crosslingual generalization @xcite @xcite . why is this effective? our results provide evidence that large-scale pretraining, even with imbalanced corpora, induces equally cross-lingualistically generalizable grammatical abstractions as pretraining with balanced corpora. this is evidence in favor of a hypothesis discussed in @xcite : the internal lingua franca of large language models may not be english words per se, but rather concepts. that said, it is still reasonable to assume these concepts are likely biased toward how english handles them in models pre-trained on imbalanced corpora. how generalizable should grammatical concept representations be? while it is more parameterefficient to share representations across languages, this may lead to biases if two languages assign different social or semantic connotations to-or simply distribute differently-the same grammatical concepts. indeed, there is not a one-to-one relationship between concepts across languages: different languages may have differing numbers of values for the same concept (e.g., finnish has many more grammatical cases than german), and may use the same categories in different ways (e.g., the same nouns often have different genders in different languages, even within language families). more language-specific features could lead to less bias, but would require more parameters to represent. it is not clear what the optimal point between these two ends of generalization are. more practically, the extent to which concepts are shared across languages has implications for multilingual downstream nlp tasks. for example, it helps explain the phenomenon that preference tuning for toxicity mitigation in a single language generalizes to other languages @xcite . thus, we might reasonably expect various types of interventions and model editing approaches to generalize from a single language to other languages.