The Same but Different: Structural Similarities and Differences in Multilingual Language Modeling

## Abstract

Abstract We employ new tools from mechanistic interpretability in order to ask whether the internal structure of large language models (LLMs) shows correspondence to the linguistic structures which underlie the languages on which they are trained. In particular, we ask (1) when two languages employ the same morphosyntactic processes, do LLMs handle them using shared internal circuitry? and (2) when two languages require different morphosyntactic processes, do LLMs handle them using different internal circuitry? Using English and Chinese multilingual and monolingual models, we analyze the internal circuitry involved in two tasks. We find evidence that models employ the same circuit to handle the same syntactic process independently of the language in which it occurs, and that this is the case even for monolingual models trained completely independently. Moreover, we show that multilingual models employ language-specific components (attention heads and feed-forward networks) when needed to handle linguistic processes (e.g., morphological marking) that only exist in some languages. Together, our results provide new insights into how LLMs trade off between exploiting common structures and preserving linguistic differences when tasked with modeling multiple languages simultaneously.

## 1 Introduction

As large language models (LLMs) have become the undisputed state of the art for building English language technology, there is decided interest in replicating their success across the full range of human languages. However, very little is known about the internal structure of LLMs, and whether such structure is conducive to acquiring broad multilingual capabilities. In fact, recent research has produced seemingly contradictory findings, such as evidence that multilingual models adopt language-specific representations (Tang et al., ; Choenni et al., ), while simultaneously showing good transfer across languages even in cases that would appear to have no superficial similarities that can be exploited to aid such transfer (Pires et al., ). Given the importance of building technology for diverse languages, there is a need for a more precise understanding of how LLMs represent structural similarities and differences across languages, and whether such representations accord with our intuitive understanding of how languages work.

In this paper, we employ tools from the growing subfield of mechanistic interpretability in order to ask whether the internal structure of LLMs show correspondence to the linguistic structures which underlie the languages on which they are trained. We focus on only the most minimal criteria of correspondence. In particular, we ask (1) when two languages employ the same morphosyntactic processes, do LLMs handle them using shared internal circuitry? and (2) when two languages require different morphosyntactic processes, do LLMs handle them using different internal circuitry? While these questions seem simple, their answers are non-obvious. LLMs readily employ overlapping circuitry for tasks that do not necessarily seem “the same” to humans (Merullo et al., ), and at the same time, neural networks frequently differentiate concepts due to surface form variation (Olah et al., ), even when humans would easily identify them as part of the same abstract category.

Using English and Chinese multilingual and monolingual models, we analyze the internal circuitry involved in two tasks, one focusing on indirect object identification (IOI) which is virtually identical between the languages, and one which involves generating paste tense verbs that require morphological marking in English but not in Chinese. Our contributions are as follows:

- •
We show that a multilingual model uses a single circuit to handle the same syntactic process independently of the language in which it occurs (§3.4).
- •
We show that even monolingual models trained independently on English and Chinese each adopt nearly the same circuit for this task (§3.5), suggesting a surprising amount of consistency with how LLMs learn to handle this particular aspect of language modeling.
- •
Finally, we show that, when faced with similar tasks that require language-specific morphological processes, multilingual models still invoke a largely overlapping circuit, but employ language-specific components as needed. Specifically, in our task, we find that the model uses a circuit that consists primarily of attention heads to perform most of the task, but employs the feed-forward networks in English only to perform morphological marking that is necessary in English but not in Chinese (§4).

Together, our results provide new insights into how LLMs trade off between exploiting common structures and preserving linguistic differences when tasked with modeling multiple languages simultaneously. Our experiments can lay the groundwork for future works which seek to improve cross-lingual transfer through more principled parameter updates (Wu et al., ), as well as work which seeks to use LLMs in order to improve the study of linguistic and grammatical structure for its own sake (Lakretz et al., ; Misra & Kim, ).

## 2 Analysis Methods

In this work, we are interested in analyzing how large language models (LLMs) trained in different languages differ in terms of the algorithms and mechanisms they invoke to handle various aspects of language processing. To do this, we employ a few recently developed analysis techniques, described below. These techniques are similar in spirit, but differ in certain details that matter for our analysis. For the most part, we find converging evidence for the paper’s main claims across both techniques. When results differ in interesting ways, we comment in our results sections.

### 2.1 Path Patching

Path patching (Wang et al., ; Goldowsky-Dill et al., ; Vig et al., ; Hanna et al., ; Tigges et al., ) has become the most standard and widely-accepted technique within the still-new subfield of mechanistic interpretability. The goal of path patching is to localize specific circuits within the weights in a trained neural network that play a causal role in model behavior. The setup requires a pair of contrastive inputs, one referred to as the clean input and the other as the corrupted input.

Path patching caches the activations for both inputs and then replaces the values of individual heads on the clean input with the values those heads would have taken had they been run on the corrupted input. In this way, the method aims to find the specific important head which maximally explains the final logits. Working backward, i.e., through patching the important heads at each layer, path patching has been used to identify full circuits that carry out the task. On its own, path patching only identifies important heads. To gain insight into the specific functions of these heads, path patching is usually used with logit attribution (Nostalgebraist, ; Belrose et al., ; Dar et al., ; Yu et al., ) which projects activations into the vocabulary space, as well as with bespoke analysis techniques invented by prior work to explain specific types of heads, such as duplicate-token detection heads or copy heads (Wang et al., ).

The advantage of path patching is primarily its wide adoption, which makes it easier to trust results, and enables us to compare with prior work in order to vet the results we are seeing (i.e., checking that we reproduce prior work when we expect to do so). The primary downside is that the method requires careful design of the minimal pair templates. The circuit that is discovered is highly dependent on how these pairs are defined. Moreover, in some of our language pairs, a comparable design of such templates is not possible (see $\S$ 4). Thus, we explore additional techniques.

### 2.2 Information Flow Routes

Given the aforementioned drawbacks of path patching, we also employ the information flow routes method (Ferrando & Voita, ; Tufanov et al., ). At each timestep, this method computes the contribution of each head to the residual streams. These heads are then aggregated over the entire attention block to construct a graph of the information flow that show which residual stream at which layer is important to the current residual stream value update across different timesteps. In this method, different from path patching where we can see circuit components across layers, the importance can only be computed every two layers. Compared to path patching, information flow routes have the advantage of not requiring minimal pairs of inputs. The tradeoff is that (1) the method is new and thus we do not benefit from clear expectations about what it should yield in certain settings and (2) this method tends to discover more generic components and larger circuits. In our paper, we thus use both information flow routes, and path patching along with logit attribution in conjunction for analysis at different granularity levels. Unless otherwise stated, both methods produce results that are consistent with the primary claims we make in each experimental section.

## 3 Tasks with Common Linguistic Structure Across Languages

### 3.1 Questions

We first ask whether large language models (LLMs) will learn to use shared circuitry for different languages, specifically for aspects of language processing that reflect similar structures across languages. That is: if two distinct languages use similar morphosyntactic structure but realize it using different lexical items, will an LLM treat these as the same (handling them with the same abstract internal circuit) or as different (handling them with distinct, language-specific circuits)? We ask this question first in the case of multilingual models, and second in the case of monolingual models.

### 3.2 Indirect Object Identification (IOI) Task

Figure: Figure 1: Attention heads activation frequency comparison between English IOI, Chinese IOI and English control tasks. Left: Comparison between English IOI and Chinese IOI on BLOOM (with specific functional heads in different marker types). Right: Comparison between English IOI and English Tense on BLOOM. For pairs of heads with non-zero activation frequency, they are shaded based on their value difference. Darker gray means smaller differences. The left graph has more number of shaded head pairs compared to the right, indicating a greater similarity between the activated heads.
Refer to caption: extracted/5920428/graph/ioi_info_flow.png

We set up our data following the original IOI paper (Wang et al., ). IOI consists of sentences such as *“Susan and Mary went to the bar. Susan gave a drink to [BLANK]”*, in which the model is expected to predict *Mary*.

These names are tokenized as one single token in the models. To diversify the data, we use fifteen different templates for the actions. We also use ChatGPT to translate the templates and names into Chinese and have them manually inspected by native speakers.

Wang et al. () identifies a specific circuit within GPT2 (Radford et al., ) for performing the IOI task. At a high level, the circuit reveals that the model runs the following algorithm: first, it identifies that there is a duplicated name (in the above example, *Susan*). Second, it inhibits attention to this duplicated name. Third, it copies the remaining (non-duplicated) name.
These steps are carried out by a set of functionally specialized attention heads: Previous Token Heads, Duplicate Token Heads, Induction Heads, S-inhibition Heads, and Name Mover Heads. Duplicate Token Heads attend to the token position of Susan and identify this name token is mentioned twice in the sentence. S-inhibition Heads inhibit the Name Movers’ attention to both occurrences of Susan. The Name Mover Heads output the remaining name (Mary). More detailed descriptions of these heads are given in prior work (Wang et al., ; Merullo et al., ). For our purposes, what is important is whether the same heads are important and perform the same roles across languages.

### 3.3 Models

We use BLOOM-560M(^1^11We refer to the model as BLOOM for simplicity below.) (Workshop et al., ) as our multilingual model. We first evaluate whether BLOOM is able to complete the task by checking if it always prefers the correct name over the incorrect name. We use both accuracy and zero-rank rate to measure the model performances. Accuracy computes the percentage of times when the probability of predicting the correct name is higher than the other name. The zero-rank rate measures the percentage of times where the correct name is the actual top answer. In the IOI task, BLOOM has an accuracy of 100% and a zero-rank accuracy of 96% in English and 95% accuracy and 93% zero-rank accuracy in Chinese. As a multilingual model, BLOOM has a high and comparable performance between Chinese and English.

For experiments with monolingual models, we select GPT2-small (Radford et al., ) as our English-only model and CPM-distilled (^2^22[https://huggingface.co/mymusise/CPM-Generate-distill](https://huggingface.co/mymusise/CPM-Generate-distill)) as our Chinese-only model(^3^33We refer GPT2-small as GPT2 and CPM for CPM-distilled below for simplicity.). These two models have the same architecture and contain the same number of parameters. GPT2-small has a 99.5% accuracy and CPM has an 84.5% accuracy. The zero-rank rate for GPT2 is 97.5% meaning that the correct name has the highest probability most of the time. CPM has a zero-rank accuracy of 57.5%. Both models are able to complete the task while the performance of GPT2 is comparably better.

Figure: Figure 2: Illustration of IOI circuits of English and Chinese on BLOOM-560M model. Blue-framed rectangles or texts are the components used in English IOI and orange-framed ones are for Chinese IOI. Heads marked in black are the shared functional heads between English and Chinese IOI. The English and Chinese IOI circuits are highly similar as they use most of the same components for the same functionality to implement the algorithms.
Refer to caption: extracted/5920428/graph/en_zh_ioi_multilingual.png

### 3.4 Results on Multilingual Model

We first validate whether the English and Chinese tasks share the same important heads. We use the information flow routes technique across 50 examples respectively in English and Chinese. We calculate the activation frequency for every head component. If one head is highly activated(^4^44Following Ferrando & Voita (), we set the threshold $\tau$ = 0.03. We consider a head is highly activated if its contribution value surpasses $\tau$.) then it indicates this head is important for the task. In Figure 1, we observe a high overlap in the important heads used between languages. The heads that are shown to be important to solve the task in English are also important in Chinese. As a comparison, we compare with the BLOOM’s activated heads from an unrelated task (specifically, the past tense task described in $\S$ 4.2) as a control. If the circuit overlap is indeed indicative of the model exploiting shared algorithmic structure, we would expect that the overlap between English and Chinese variants of the same linguistic task should be higher than the overlap between English for two tasks that invoke dissimilar linguistic (i.e., syntactic and semantic) structures. Indeed, compared with the high overlap between the IOI task in Chinese and English, significantly different heads are used in the control task. To measure the similarity between the activation patterns, we compute the Pearson Correlation Coefficient $\rho$ (Freedman et al., ) between the head activation frequency for the two task pairs. While $\rho$ between English and Chinese IOI task is 0.72, $\rho$ between English IOI and English control task is 0.48. Anecdotally, we observe that many of the heads that are shared between English IOI and control are also shared with Chinese IOI. This suggests that these might be generically important heads which we expect to appear in a wide range of tasks, although further work is needed to test this hypothesis quantitatively.

We continue to examine if these shared functionalities are the same as the ones defined in Wang et al. (). In Figure 2, we identified functionalities of the important components in Figure 1 in Chinese and English in BLOOM. We first use path patching to identify the components and then use the specific metrics mentioned in Wang et al. () to validate their functionalities. Both of these experiments return highly similar results(^5^55Detailed analysis can be found in §B.1). The functionalities we identify align with Wang et al. (). This suggests that not only is the mechanism (i.e., the heads and their functionality) shared across English models (GPT2 and BLOOM), but moreover this same mechanism is shared nearly exactly between Chinese and English within BLOOM.

Figure: Figure 3: English and Chinese IOI task circuits. Blue highlights are components used and inputs for the English task in GPT2. Orange highlights are for CPM. The components with both blue and orange frames are shared in both the English and Chinese circuits. In each of these heads, blue texts indicate the heads in the English Circuit and orange in Chinese. The Negative Name Mover heads appear only in the English model. Copy Suppression Head appears only in the Chinese model. Despite trained on completely different data, models still implement largely similar algorithms to solve tasks in different languages.
Refer to caption: extracted/5920428/graph/en_zh_ioi_monolingual.png

### 3.5 Results on Monolingual Models

While the cross-lingual circuit overlap in a multilingual model is not obvious (i.e., it requires abstracting over patterns that manifest using disjoint sets of surface forms), it is also reasonable to assume that the convergence stems from the model’s broader multilingual training data and objective. We thus ask whether similar patterns arise even when models are trained independently in English and in Chinese. For this analysis, as mentioned, we use the English-only model GPT2-small and a distilled version of the Chinese-only model CPM-Generate (Zhang et al., ) for their exact same architecture.

We discover circuits with path patching which gives similar results and identifies the same components that are important for the circuits. In both GPT2 and CPM, we identified similar circuits as Wang et al. () outlined in Figure 3. This is surprising as it suggests that at a high level, the LLMs consistently converge to highly similar algorithms for solving this type of task, even when trained on very different languages. That said, we do observe some differences between the circuits. In GPT2, Wang et al. () identifies Negative Name Mover Heads. These heads write negatively in the direction of the name tokens. Similarly, Negative Name Mover Heads are found in the circuit in GPT2. However, we didn’t observe any Negative Name Mover Heads in CPM, instead of observed the Copy Suppression Head. Such heads are identified by McDougall et al. () in the Pythia model (Biderman et al., ). Instead of reading the information flow from the S-inhibition head, Copy Suppression Heads read directly from the Duplicate Token Heads and suppress the logit of the repeated token in the output. This divergence is not able, and could provide a good starting point for future work on predicting when and how specific circuitry emerges during training (see Discussion §6).

## 4 Tasks with Language-Specific Structure

### 4.1 Questions

The above results show that LLMs are capable of recognizing structural parallels across languages, and even that monolingual models, trained without explicit influence from other languages, converge on similar algorithms for similar structures. We next ask: what happens when languages exhibit dissimilar structures? In reality, languages exhibit immense morphological and syntactic variation, and there is never a perfect one-to-one correspondence between the structural elements of different languages. A straightforward example of this phenomenon is the fact that English includes morphological markers for tense (e.g., walk vs. walked) while Chinese does not. We thus ask, given such variation, do LLMs adopt different circuitry for different languages, or rather do they continue to invoke shared circuitry but employ language-specific subroutines as needed?

### 4.2 Past Tense Task

We construct minimum pair templates in English: “*Now I $\text{verb}_{\text{present}}$. Yesterday I also $\text{verb}_{\text{past}}$.*” and “*Yesterday I $\text{verb}_{\text{past}}$. Now I also $\text{verb}_{\text{present}}$.*”. For Chinese, we construct the exact same template in Chinese “*现在我$\text{verb}_{\text{present}}$。昨天我也$\text{verb}_{\text{past}}$。*” and “*昨天我$\text{verb}_{\text{past}}$。现在我也$\text{verb}_{\text{present}}$。*”(^6^66现在-Now，昨天-Yesterday, 我-I, 也-also). To collect the verbs, we start with the English verb tense dataset from Big-bench (Srivastava et al., ) and filter the verbs to only retain those with regular inflection rules. Then all English verbs are translated, deduplicated and filtered with native speakers’ manual inspection. Notice that in Chinese, there are no morphological markers between *$\text{verb}_{\text{present}}$* and *$\text{verb}_{\text{past}}$*. Both of them refer to the stem form of the verb. By leveraging this distinction between the English and Chinese languages, we are able to investigate the mechanistic differences required when models process semantic equivalent tasks but with different morphosyntactic rules. Our analysis on this task focuses more specifically on multilingual models instead of monolingual models since they allow us to more directly observe the structral similarities between the circuitries.

Figure: Figure 4: (a) Head activation frequency comparison between English and Chinese past tense task on Qwen. (b) Illustration of the tense circuits in Qwen that contains three components we focus on: copy heads, past tense heads and feed-forward layers. (c) Top promoted tokens from copy head and past tense head. Words highlighted in either blue and orange are expected model predictions. Gray tokens are the specific tokens that the head promotes. Here the copy head is 21.3 and the past tense head is 19.4. As shown in (a), the copy head has a similarly high activation frequency in both languages whereas the past tense head is only frequently activated in English.
Refer to caption: extracted/5920428/graph/tense_coarse.png

### 4.3 Models

We use Qwen2-0.5B-instruct(^7^77We refer to the model as Qwen below for simplicity.) (Yang et al., ) for the task for its better benchmark performance compared to BLOOM(^8^88Here we replace the model of interest from BLOOM to Qwen. Qwen is similarly a multilingual language model but with a strong language focus on both English and Chinese. It achieves comparable performances in English compared to BLOOM but significantly better performance in Chinese, which is necessary for extracting the circuits. See Table 1 in the Appendix for the performance comparison.). The final verb collection contains 62 English verb instances and their Chinese translation and themselves are both single tokens after being processed by the Qwen tokenizer. For the English task, Qwen reaches 96.77% accuracy for the normal input and 58.06% for the zero-rank rate. In Chinese, as there are no minimal pairs, we can only obtain a zero-rank rate of 25.81%.

### 4.4 Results

In Figure 4, compared to the active heads distribution on the IOI task, we notice that some early-layer heads have high activation frequencies that are shared in both English and Chinese tasks. However, there are much fewer shared heads in the later layers of the model. Specifically for the Chinese task, we observe that almost no heads have high activation frequency in the later layers (Layer 19-20). We posit that some heads that are shown to be more active in these later layers are responsible for the additional inflection rule unique to the English task in our setting, which is unnecessary to the Chinese task.

### 4.5 Function-Specific Heads

Through path patching, we observe that heads 19.4 and 19.5(^9^99We use layer.head for referring to specific heads of interest.) exert the most significant influence on the final logits (Refer to Appendix C for details). Examining the top promoted words from the most positive projections into the model vocabulary space (Figure 4 (c)), we find that they generally favor past-tense words in English (e.g., was, were, and other past-tense verbs unrelated to the target verb). Among the top-promoted tokens, we also notice Chinese verbs with the suffix ”了” (e.g., 进行了，can be translated to “done”.), where ”了” is commonly used to indicate the completion of an action. Even when provided with Chinese input, these heads continue to strongly encode past-tense concepts. In Figure 4(a), head 19.4 appears frequently activated for English but not for Chinese, suggesting that these heads are predominantly engaged in English processing rather than in Chinese.

To validate our findings, we ablate the heads 19.4 and 19.5 and analyze their impact on the tasks. The rank of correct past tense verbs in English remains relatively unchanged. Whereas other non-relevant past tense verbs move backward slightly to make spaces for the corresponding present tense verbs, resulting in their ranks getting promoted by an average of 83.21 positions. In the Chinese task, the rank of the correct verb moves forward by an average of 4.58 positions. Notably, after ablating these past-tense heads and projecting the final-layer logits to the vocabulary space, present-tense verbs emerge as the second most probable token (See Figure 11 for details). These results suggest that in English, past-tense heads actively suppress present-tense verbs to disambiguate between verb tenses. However, these heads do not play a similar role in the Chinese task.

In Ferrando & Voita (), they mention that patching can only emphasize the heads that are important for the original tasks but not the contrastive baseline. In the past tense task, we locate past tense heads that are in charge of disambiguating past tense verbs from present tense ones via path patching. These heads are discovered due to the additional morphological markers between the original and contrastive sentence. However, how do models know to promote that specific verb (with or without the marker) without confusing it with others?

To address this question, we compute the total score of each head projecting onto either past tense or present tense verb groups (see Appendix C.1). The primary heads identified are 21.3, 21.4, and 21.13. We observe that these heads not only emphasize different morphological variants of the target verb but also promote tokens that are orthographically similar or semantically related, such as synonyms and antonyms (see Figure 11 in Appendix). These heads are crucial for both English and Chinese tasks. To assess their impact, we perform ablation studies on these heads and observe significant impacts on model performance. In English, the average rank of past tense verbs drops by 141.95, while present tense verbs experience a much larger average demotion of 221.39. This disparity suggests that past tense verbs are less affected, possibly because the identified past tense heads remain active, suppressing present tense verbs. As shown in Figure 11, the top-promoted words after ablation are still predominantly general past tense verbs. For Chinese, the model performance also declines, with verbs in general experiencing an average rank demotion of 201.82. These ablation results indicate that the copy heads are essential for both English and Chinese tasks, while the past tense heads are specifically activated in English, where they are crucial for task completion. However, qualitative inspection also suggests that the semantics of the verb is not entirely decoupled from the tense, highlighting a need for further work in order to produce more nuanced descriptions of the function of these heads in this circuit, and in general.

Figure: Figure 5: Illustration of the effects of late feed-forward layers ablation (layer 20-23). Left: Zero-rank rate changes comparison between English and Chinese past tense task. Right: Top predicted tokens comparison for before and after ablation. We observe that English performance is significantly impacted after ablation but Chinese is barely influenced. Qualitative results on the right show that the correction token move backward in English but remain the top prediction in Chinese.
Refer to caption: extracted/5920428/graph/mlp_ablation.png

### 4.6 Feed-forward Networks

Previous work (Merullo et al., ) discusses the role of the feed-forward networks (FFN) in retrieving the correct transformation required for the past tense tasks. Specifically, these FFNs are found in the last few layers of the models where the ranked position of the present-tense verb and past-tense verb switch. Comparing the English and Chinese tasks, we assume that these FFN layers play a role in predicting the past-tense verb in English but remain unactivated for the Chinese task. When we ablate out the FFNs from layer 20-24, the accuracy on the English task drops from 97.44% to 47.44% and the zero-rank rate from 58.06% to 17.74%. However, for the Chinese task, the zero-rank rate is barely changed (25.08% to 24.19%).(^10^1010Note we cannot compute accuracy in Chinese as there is no past-tense alternative verb form against which to compare.)

In the English example, when examining top predicted tokens qualitatively in Figure 5, we see that the rank of the correct past tense answer *visited* moved back by 1 position after ablating the MLP layers. Additionally, it is worth noting that morphological variants (E.g. *visit*, *visits*, etc.) or semantically relevant tokens (E.g. 参观, 访问(^11^1111Both words can be translated to *visit*.)) move to the top predictions. These are also tokens that are actively promoted by the copy heads mentioned earlier. In the Chinese example, we see that the top token as the correct prediction still remain at the same position, unaffected by the FFN layers removal.

## 5 Related Work

#### Mechanistic Interpretability

We built on previous work on mechanistic interpretability to reverse engineer the mechanism of neural network. Circuits are a significant paradigm of model analysis that has emerged from this field. Originated from the vision model (Olah et al., ) and continued to transformer language model (Meng et al., ; Wang et al., ; Hanna et al., ; Varma et al., ; Merullo et al., ; Lieberum et al., ; Tigges et al., ), works in mechanistic interpretability try to characterize the function of individual components in completing certain tasks. Beyond the work in understanding the role of attention heads (Olsson et al., ; Chen et al., ; Singh et al., ; Gould et al., ; McDougall et al., ), efforts are made in understanding neurons and feed-forward network in
(Vig et al., ; Finlayson et al., ; Sajjad et al., ; Gurnee et al., ; Voita et al., ; Merullo et al., ). These studies mainly use causal methods with minimal pairs to locate the components and their functionalities (Vig et al., ; Chan et al., ; Geiger et al., ; ; Meng et al., ; Wang et al., ; Chan et al., ; Cohen et al., ). However, these methods heavily rely on manual inspection, which limits their scalability to larger models. Therefore other recent works have attempted to automate the causal process (Conmy et al., ; Bills et al., ; Syed et al., ; Hanna et al., ) or use input attribution to trace the subgraph of important components lifting the burden of defining minimal pairs (Ferrando & Voita, ).

#### Multilingual Interpretablity

With English-centric large language models demonstrating outstanding generation and reasoning capabilities, more current efforts have been devoted to expanding such capabilities to multilingual settings (Workshop et al., ; Yang et al., ; Üstün et al., ; Aryabumi et al., ). Many works (Wu & Dredze, ; Winata et al., ) have leveraged transfer learning to boost performances on low-resource languages as an alternative to tackling data deficiency problems. Meanwhile, language models have also been reported to suffer from the “curse of multilinguality” (Conneau, ; Chang et al., ) where languages interfere with each other given fixed model capacities. Previous works have attempted to empirically quantify the cross-lingual effects across languages (Malkin et al., ; Choenni et al., ). More recent works such as Wendler et al. () observe that multilingual models go through language conversion phrases with the reasoning process mainly conducted in English. Tang et al. () specifically locates these language-specific neurons and manipulates the output language by activating or deactivating different language-specific neurons. Li et al. () also observe that Multi-Layer
Perceptron (MLP) is associated with how toxic concepts are encoded crosslingually. However, it remains unclear how models utilize their components when receiving inputs in different languages: when a task is prompted in different languages, are the underlying circuits shared or does there exist completely separate paths? While Merullo et al. () has reported the reuse of circuit components across different tasks in the same language, no prior work has investigated this phenomenon at the cross-lingual level. Additionally, considering language construction is unlikely to be exactly the same across languages, how are model components used to reflect these differences? In this paper, with the help of mechanistic interpretability techniques like path patching and information flow routes, we provide an initial analysis to quantify to what extent model components are shared when solving tasks for different languages.

## 6 Discussion and Conclusion

We investigate when and how large language models (LLMs) encode structural similarities between languages. Specifically, we ask (1) whether linguistic tasks that are structurally similar across languages are handled by the same LLM circuitry and (2) whether linguistic tasks that employ language-specific structure are handled by specialized LLM circuitry. Through a series of experiments employing different tasks and models in both English and Chinese, we find that indeed cross-lingual structural similarity is represented within the LLM using shared circuits which invoke the same algorithm independent of language. We find that these shared algorithms emerge even in monolingual models trained on different languages. We also show that when tasks require language-specific components–specifically past tense morphology in English–LLMs localize the corresponding processing such that it plays a causal role in some languages but not in others.

Taken together, our results provide a first step in using mechanistic analysis about LLMs inner workings in order to ask higher-level questions about how linguistic structures are reflected in state-of-the-art AI models. Our results are limited to specific tasks and datasets, future work will be necessary to replicate and generalize our findings. Even so, our studies yield several interesting and encouraging insights which can inspire new lines of inquiry.

For example, we find that the same circuit emerges across different models trained on entirely different languages. This raises questions about what type of structure is shared across otherwise-dissimilar languages, or even shared universally? That is, what regularities exist in the distribution of both English and Chinese such that they give rise to the same highly specific functional components organized in the same way? While the details of this discussion are specific to LLMs, the spirit shares much with long-running linguistic discussions of universality and of learnability in language (Yang, ). Deeper cross-disciplinary collaborations could possibly exploit LLMs in order to gain fresh perspectives on such questions.
Another important direction concerns practical considerations for developing state-of-the-art technology in languages other than English. This includes not only building LLMs in other languages (Wu et al., ), but also ensuring their safety and robustness (Yong et al., ; Deng et al., ) across languages. Insights into specific mechanisms that are shared between languages could guide more principled approaches to transferring parameters or anticipating failure modes. Future work could explore whether circuit overlap like what we document here can be exploited towards these goals.

## Appendix A Task Benchmark Performances

In this section, we present the accuracy and zero-rank rate of both the IOI tasks and Past Tense tasks discussed in $\S~{}\ref{sec:ioi_sec}$ and $\S~{}\ref{sec:tense}$. In Table 1, we see that all monolingual models and multilingual models perform well on the IOI tasks. For the past tense task, BLOOM obtains good performance in English but not in Chinese. In comparison, Qwen performs slightly worse in English but much better in Chinese. As a result, we focus on Qwen for our language-specific analysis.

**Table 1: Model performances for the IOI and past tense tasks. Note that we are unable to compute accuracy for the past tense task in Chinese (marked by -) due to the absence of minimal pairs.**
| Model | Lang. | Accuracy | Zero-Rank Rate |  |  |
| --- | --- | --- | --- | --- | --- |
| IOI Task |  |  |  |  |  |
|  |  | normal | flipped | normal | flipped |
| GPT2-small | EN | 99.5 | 99.5 | 97.5 | 95.5 |
| CPM-Generate-distill | ZH | 84.5 | 86.5 | 57.5 | 73.0 |
| BLOOM-560M | EN | 100.0 | 100.0 | 96.0 | 91.0 |
| BLOOM-560M | ZH | 95.0 | 100.0 | 93.0 | 99.5 |
| Past Tense Task |  |  |  |  |  |
|  |  | normal | flipped | normal | flipped |
| BLOOM-560M | EN | 90.3 | 51.6 | 80.7 | 37.1 |
| BLOOM-560M | ZH | - | - | 12.9 | 1.6 |
| Qwen2-0.5B-instruct | EN | 96.8 | 69.4 | 58.1 | 24.2 |
| Qwen2-0.5B-instruct | ZH | - | - | 25.9 | 21.0 |

## Appendix B Patching Details of the IOI Task

### B.1 Multilingual Models

To extract the IOI circuits on the BLOOM model, we use the transformer-lens library (Nanda & Bloom, ) and follow the practices of Wang et al. (). Figure 6 shows the main steps to discover specific function heads for both English and Chinese tasks on BLOOM. Throughout each of the steps, we notice the important heads discovered between English and Chinese are highly similar, leading to largely overlapping circuits. Figure 7 validates the functions of the Duplicate Token Heads, Previous Token Heads and Induction Heads found by showing their attention scores on sequences of random tokens. Please refer to more detailed definitions and experiment designs on these metrics in Wang et al. ().

Figure: Figure 6: Path Patching steps for extracting IOI circuits on BLOOM-560M. Left: English Input. Right: Chinese Input.
Refer to caption: extracted/5920428/appendix/bloom_patching.png

Figure: Figure 7: Metrics for Duplicate Token Head, Previous Token Head, and Induction Head on English Inputs (Top) and Chinse Inputs (Bottom).
Refer to caption: extracted/5920428/appendix/bloom_head_scores.png

### B.2 Monolingual Models

For path patching on CPM, we add the CPM models to the transformer-lens library and follow the same steps as the multilingual model described above. Figure 8 presents the comparison between English and Chinese inputs when we patch to the residual stream on GPT2 and CPM respectively. Notice that here the heatmap patterns are quite different because they are trained on completely different data despite the same architecture (with the same number of layers and heads). Then we perform patching to individual heads and follow Wang et al. () to find out their functionalities. Figure 8 Right shows the attention patterns of the name mover heads and copy suppression heads. We see that mover heads 8.9 and 9.10 show copying behaviors toward the IO tokens and 11.7 shows copying behavior toward both IO and S tokens. The copy suppression head 11.2 on the other hand suppresses the copying action of the S token. Figure 9 shows our validation experiments on the functions of Induction heads, Duplicate Token Heads and S-inhibition heads. Specifically for the S-inhibition head 7.0, when we ablate the head, the original name movers stop preferring the IO tokens but start to promote both IO and S tokens.

Figure: Figure 8: Left: Path patching comparison between GPT2 on English task and CPM on Chinese task. Right: Attention probability vs. head projection output into the IO and S space.
Refer to caption: x1.png

Figure: Figure 9: Top: Induction head scores on random tokens and its attention pattern. Middle: Duplicate token attention patterns on ABAB random strings. Bottom: S-inhibition head ablation effects on name movers.
Refer to caption: x2.png

## Appendix C Patching Details of The Past Tense Task

### C.1 Path Patching

For the Past Tense task, we conduct path patching with the English templates since there are no minimal pairs available for Chinese. As shown in Figure 10 Left, two heads on layer 19 are the most prominent. We later use head attribution and see that they are past tense heads that pay attention to general past-tense concepts and suppress present-tense verbs (shown in Figure 11). In Figure 10 Right, we project all the head outputs to the vocabulary space and see how much each head writes to the direction of either past tense verbs, present tense verbs in English, or verbs in Chinese. Again we notice that the patterns are similar and a few heads on Layer 21 writes strongly to both past tense and present tense verbs. These heads also show up in the path patching results but do not influence the final logits as strongly as the past tense heads. By examining the top-promoted tokens by these heads, we see that these heads promote orthographically similar or semantically adjacent tokens. These could contain verb forms with various morphological markers as well as synonyms and antonyms (See Figure 11). More detailed discussions can be seen in $\S~{}\ref{sec:head_functions}$.

Figure: Figure 10: Left: Path Patching on English Past Tense Task. Right: Verb Promotion Heatmaps for English and Chinese Past Tense Task.
Refer to caption: extracted/5920428/graph/tense_patching.png

### C.2 Ablation Experiments

Figure 11 shows rank changes on the verbs and contains qualitative examples when each type of the function-specific heads are zero-ablated. In general, we notice that past tense heads are specifically activated for English tasks only and therefore the rank of past tense verbs moves forward when these heads are ablated. However, the prediction does not get impacted when we ablate these heads for the Chinese task. Copy heads, on the other hand, are important for both English and Chinese tasks. Past-tense, and present-tense verbs in English and Chinese verbs are all demoted significantly when copy heads are ablated, indicating these heads are shared components implementing the same functions for both English and Chinese circuits.

Figure: Figure 11: Effects of Past Tense Heads and Copy Head Ablation.
Refer to caption: extracted/5920428/graph/head_ablation.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
questions: 1. **Scope of Claims:**  
    Could the authors clarify why they make general claims about multilingual models despite the case-study limitations? Would they consider rephrasing these claims to better align with the study's focused scope? Is there a little clash between a generality of the paper title and strict case-study experimental approach?  
    
2. **Support or Contrast with Alignment-based studies**
	How do the authors’ findings support or differ from prior studies on multilingual alignment, especially those focusing on hidden state alignment? 

3. **Comparison with Coarse-Grained Studies:**  
    How do the findings here align with or diverge from conclusions in prior, coarse-grained studies on cross-lingual interpretability? Can conclusions from prior work with support from this work's experiments together support general claims or gain novel broader conclusions?

4. **Choice of Circuit-Level Analysis:**  
    While I am very much in favor of it, I am interested to hear more about why the authors chose a circuit-level approach over other methods and what alternative methodology options they considered. What other mech interp approaches would help support the authors claims in future work (e.g. analysis with SAEs)?  
    
5. **Broader Application:**  
    Do the authors have recommendations on which additional languages, models, or tasks would best extend this study’s findings?
soundness: 3
strengths: _Clarity:_  
The paper is clear and well-written, with high-quality illustrations and concise, precise language. The flow is logical, and the key points are emphasized effectively.

_Quality:_  
The experiments are well-designed and effectively executed. The choice of mechanistic interpretability techniques to explore cross-lingual overlap is sound, and the methods are applied accurately. The IOI and past tense tasks are suitable examples; IOI builds on established monolingual work, while the past tense task introduces unique, language-specific morphological challenges.

_Originality:_  
This work is notable for its detailed, fine-grained circuit-level analysis of cross-lingual overlap in models.

_Significance:_  
For the NLP community, particularly those working on cross-lingual models and low-resource languages, this work provides a valuable deeper understanding of cross-lingual transfer down to the circuit level. This detailed approach might inspire new techniques for more effective cross-lingual transfer.
summary: This paper aims to explore how similar or different circuits are across languages for linguistic tasks in language models. It takes a case-study approach, focusing on two tasks (IOI and past tense task), two languages (English and Chinese), and three language models (two for IOI and one for the past tense task). The past tense task involves morphology, which allows the authors to analyze how it’s handled in different languages.

Using techniques like path patching, the authors identify circuits and find significant overlaps between languages for both tasks. They also highlight interesting differences, like specific "past tense" heads in English.
weaknesses: _Scope of Main Claims:_  
In my view, the primary contribution of this work lies in _demonstrating a specific case study_ of circuit-level cross-lingual transfer between languages in related or separate models. While the paper aims to make broader claims about multilingual models, the setup—two languages, two/three models, and two tasks—suits a focused case study rather than generalizable conclusions. Although the paper acknowledges these limitations, the claims in the introduction could be adjusted to better reflect the scope of the experiments.

Expanding to more languages, models, and tasks would strengthen broader claims, but as a focused case study, this paper is effective on its own. I would recommend reframing the claims to align more closely with the experimental scope.

_Related Work on Multilingual Interpretability:_  
A broader review of related work could enrich the discussion, especially on different approaches that analyze multilingual models by aligning internal representations. For instance, [1] trains a monolingual transformer LM and transfers it to another language by retraining only the embedding matrix, hinting at cross-lingual structure similarities. This aligns with the monolingual IOI task analysis in this paper. [2] and [3] conduct coarse-grained analyses on similar cross-lingual interpretability challenges, while [4] aligns hidden states, and [5] provides further insights into the universality of these findings. [6] surveys cross-lingual alignment, offering broader yet less detailed conclusions on the inner workings of multilingual models. This paper can be seen as building on this older work by diving deeper into specific attention heads and showing detailed cross-lingual structures.

It would also be helpful to add a few sentences on how the conclusions here either support or contrast with prior work beyond the works already mentioned.

**References**

1 [On the Cross-lingual Transferability of Monolingual Representations - ACL Anthology](https://aclanthology.org/2020.acl-main.421/)

2 [First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT](https://aclanthology.org/2021.eacl-main.189)

3  [Emerging Cross-lingual Structure in Pretrained Language Models - ACL Anthology](https://aclanthology.org/2020.acl-main.536/)

4 [[PDF] Similarity of Sentence Representations in Multilingual LMs: Resolving Conflicting Literature and a Case Study of Baltic Languages | Semantic Scholar](https://www.semanticscholar.org/paper/Similarity-of-Sentence-Representations-in-LMs%3A-and-Del-Fishel/0b749f70dd8cbe22fcfa7efeb571836b195c125d)

5 [Cross-lingual Similarity of Multilingual Representations Revisited - ACL Anthology](https://aclanthology.org/2022.aacl-main.15/)

6 [Understanding Cross-Lingual Alignment—A Survey - ACL Anthology](https://aclanthology.org/2024.findings-acl.649/)

---

confidence: 4
contribution: 4
initial_rating: 8
presentation: 4
questions: (1) By “ablate”, I assume you mean zeroing out the contribution of a head to compare performance with/without the head? I suggest that you just mention t what you mean by “ablate” explicitly somewhere, since you use the word quite often in discussion your results e.g. “To validate our findings, we ablate the heads 19.4 and 19.5 and analyze their impact on the tasks.”

(2) Section 4.5 can be organised a bit better. It contains quite a lot of information and requires the reader to refer to many figures. Potentially split the large paragraph (lines 403–418) into multiple subparagraphs, or organise the section into paragraphs with headings.

(3) Line 297: Fix wording of “This divergence is not able,”.
soundness: 3
strengths: I'd like to congratulate the authors on an excellent and super interesting paper!

(1) The analysis conducted by the authors is detailed, extensive, and extremely well presented. It provides concrete insights into the mechanisms underlying multilingual LLMs.

(2) The methodology followed by the authors is sound and extensively documented, providing a valuable framework and example for future researchers to investigate the circuitry of multilingual LLMs.

(3) The paper makes valuable contributions to multilingual interpretability, successfully unifying aspects of mechanistic interpretability with linguistically informed analysis.
summary: This paper compares how LLMs process syntactic phenomena in English vs Chinese. The authors use two tools for analysing the mechanisms underlying LLM computations, namely path patching and information flow routes. They consider two syntactic phenomena, one shared across English and Chinese and another unique to English. For the shared syntactic phenomenon (indirect object identification), they show that LLMs use similar internal circuitry across both languages. For the language-specific phenomenon (morphological markers in English only), they show that circuitry diverges. The results show that multilingual LLMs employ overlapping circuitry for linguistic phenomena common across languages, and that similar internal mechanisms emerge for related syntactic processing even in monolingual models for different languages. The paper also presents a detailed analysis of the LLM computation involved in the studied syntactic processes.
weaknesses: Due to the heuristic nature of mechanistic interpretability tools like path patching, it’s difficult to conclude with certainty that findings such as those presented in this paper will generalise to other tasks/languages/models. However, the authors are careful to not overstate their claims.

---

confidence: 2
contribution: 3
initial_rating: 5
presentation: 3
questions: Do you have any initial ideas on how to conduct cross-disciplinary collaborations based on this work?
soundness: 3
strengths: **Summary Of Strengths**:
- Interesting topic and findings: I find it very attractive to use mechanistic interpretability to identify the relationship between models and language structures. The conclusion about common components between multilingual and monolingual models provides insightful ideas for further research.
summary: **Paper Summary**:
- This paper shares some interesting findings on the similarities and differences between the internal structure of LLMs and the linguistic structure of English and Chinese. The authors employ mechanistic interpretability tools to explore the internal circuitry used by multilingual and monolingual LLMs when dealing with the same or different morphosyntactic processes. The findings indicate that there are common circuits and trade-offs when preserving linguistic differences.
weaknesses: **Summary Of Weaknesses**:
- Limitation: The work is based only on Chinese and English and is limited to a few tasks and datasets. The conclusions could be more convincing if more languages were included.
- Clarity on future impact: It may be challenging to transfer the learning from this work to current multilingual LLM training efforts, which underscores the need to better understand the findings (e.g., cross-lingual transfer learning, parameter migration, etc.).

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Could you provide some detail for how the attention head types and arrows in Figures 2 and 3 were identified? The figures imply fairly strong claims about how the internal algorithms work, so a description of the method used to identify the directed graph feels somewhat important.
soundness: 3
strengths: 1. The tasks and methodology appear sound and well thought-out.
2. The paper is well-written, although some additional details would be very helpful (see weaknesses below).
3. The ablation results are convincing, e.g. FFN ablation in Section 4.6 dropping performance specifically for English. Also Figure 11 in the Appendix showing the effect of ablating different attention heads.
4. The results are interesting; language-agnostic and language-specific components have previously been proposed in multilingual language models (e.g. https://arxiv.org/abs/2205.10964; https://arxiv.org/abs/2109.08040; https://arxiv.org/abs/2009.12862), but the concrete demonstrations in this paper (specific circuits and their functions) are very informative.
summary: The paper assesses whether English and Chinese monolingual and multilingual language models use similar circuits for similar morphosyntactic processes (indirect object identification; IOI) and different circuits for differing processes (past tense inflection). They use path patching and information flow routes to identify circuits. They find that similar circuits are used for IOI in English and Chinese, for both monolingual and multilingual models. Overlapping circuits (in this case copy heads in attention) with language-specific components (in this case MLP blocks for English inflection) are used for past tense inflection.
weaknesses: 1. The results in Section 3.5 and the last paragraph of Section 3.4 ("We first use path patching to identify…"; p. 5) are difficult to interpret without a good amount of background from Wang et al. (2023). A high level overview of how the different types of attention heads are quantified would be extremely helpful.
2. Related to the previous point, the results indicating similar circuitry (Figures 2 and 3) rely quite a bit on the reader trusting that the algorithms visualized in Figures 2 and 3 are accurate. The description of how the head types and arrows are determined is fairly minimal ("In both GPT2 and CPM, we identified similar circuits as Wang et al. (2023) outlined in Figure 3"; p. 5), so evaluating the validity of the algorithms identified in the figures is difficult.
3. Minor point: "Anecdotally, we observe that many of the heads that are shared between English IOI and control are also shared with Chinese IOI... further work is needed to test this hypothesis quantitatively" (p. 5). It could be helpful to have some very preliminary quantification, e.g. n% of the shared heads from English IOI and control are also shared with Chinese IOI, given some threshold for similarity.
4. Consider moving Figure 11 into the main text if possible, since it is referenced several times in Section 4.5.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: - Does tokenization affect your experimental setting? More specifically, do you require that the queried property (object, past tense verb) be represented as one token?

- Why don’t you present the results for IOI with Qwen2-0.5B-Instruct?

- Why do you filter the English verbs with irregular past inflection?
soundness: 2
strengths: - The work tackles the timely problem of interpreting LMs’ functions across languages. Despite the community's focus on interpretability and multilingual models, not many works have tried to explain the mechanisms behind LMs' multilingualism.


- The authors put the work in the context of past approaches in circuit identification (especially Wang et al. 2023), extending the experimental settings to multilingual models.


- The experimental part studies the identified circuits in-depth: comparing across languages the overlap of heads in the circuits and the information flows.
summary: The submission uses interpretability tools to examine if the LLM’s mechanisms behind solving linguistic tasks are similar or differ across languages. Following Wang et al., 2023, the authors use path patching to identify information circuits in multilingual models corresponding to the same morphosyntactic task in two languages. The aim of the experiments is to examine whether such circuits will be the same (or similar) for solving tasks in distinct languages.
The paper focuses on two tasks: object identification and verb past tense inflection in English and Chinese. The performance in the tasks is checked via generic prompt filling. Experiments cover two multilingual models: BLOOM (in case of object identification) and Qwen2-0.5B-Instruct (verb past tense inflection).

The results show that in fact, the found circuits are similar across languages, suggesting that multilingual language models implement a shared mechanism for solving considered linguistic tasks across two languages (Chinese and English).
weaknesses: - The linguistic scope of the experiments is limited. The method is applied only to two relatively simple morphosyntactic tasks in two languages. The results are close to 100% in object identification, which is a sign of saturation (a more complex task would be more informative). Only two distinct languages are considered. Therefore, the result does not show whether typological similarity could affect the similarity of the identified circuits.

- The choice of models used for experiments is chaotic. It is not clear why Qwen2-0.5B-Instruct is not considered in the object identification task. The paper should present the results for both tasks, at least for two considered models.

- The methodology should be described in more detail, focusing on its implications in the multilingual setting. For instance, I understand that the head types were already described in the work of Weng et al. 2023, but the natural question is what linguistic role these types have across different languages.

Meta-review: The paper presents a mechanistic interpretability study of large language models (LLMs), focusing on the differences in language processing between English and Chinese. By analyzing the internal mechanisms of LLMs trained on both languages, the authors aim to uncover the underlying factors that contribute to performance disparities and linguistic nuances. The strengths of the paper lie in its novel approach to cross-linguistic analysis, robust experimental design, and the insightful findings that advance our understanding of multilingual language models. Reviewers noted the thoroughness of the study and its contribution to the field of interpretability. Weaknesses include concerns about the choice of LLMs used, the need for better control over data and model scale to isolate variables effectively, and a recommendation to clarify the scope of the claims made. Minor presentation issues were also identified but deemed fixable. The paper offers significant value by shedding light on important differences in language processing between English and Chinese within LLMs. The most important reasons for accepting this paper are its innovative methodology, the strength of its empirical results, and its potential to inspire further research in multilingual model interpretability.

Decision: Accept (Poster)