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

Authors: Ruochen Zhang, Qinan Yu, Matianyu Zang, Carsten Eickhoff, Ellie Pavlick
Primary area: interpretability and explainable AI
Keywords: multilinguality, interpretability

Abstract:
We employ new tools from mechanistic interpretability 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? In a focused case study on 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 are revealing about how LLMs trade off between exploiting common structures and preserving linguistic differences when tasked with modeling multiple languages simultaneously, opening the door for future work in this direction.

Decision: accept (poster)
Reviewer scores: 8, 8, 6, 6, 5

Reviews and discussion:
<thread>
creation date:
1730217004789

thread_type:
official_review

<thread_object>
creator: reviewer_5yhy

<content>
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).

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.

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.

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?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730313835588

thread_type:
official_review

<thread_object>
creator: reviewer_n6vb

<content>
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.

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.

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.

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.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730704183699

thread_type:
official_review

<thread_object>
creator: reviewer_qgek

<content>
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.

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.

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.).

questions:
Do you have any initial ideas on how to conduct cross-disciplinary collaborations based on this work?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730717003462

thread_type:
official_review

<thread_object>
creator: reviewer_eexz

<content>
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.

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.

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.

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,”.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730729512239

thread_type:
official_review

<thread_object>
creator: reviewer_p3m4

<content>
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.

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.

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/)

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?
</content>
</thread_object>

</thread>
<thread>
creation date:
1732592282765

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
On the Scope of Main Claims

comment:
We would like to thank Reviewer P3M4, EEXZ, qgEk, 5YHy for their suggestions on scoping the main claims of the paper. We have modified the abstract and introduction of the paper to emphasize that our findings and observations are based on the focused study of the English-Chinese pairs on selected language-generic and language-specific tasks.
</content>
</thread_object>

</thread>


Metareview:
metareview:
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.