Exploring the Translation Mechanism of Large Language Models
Authors: Hongbin Zhang
Kehai Chen
Xuefeng Bai
Xiucheng Li
Yang Xiang
Min Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Large Language Model
Multilingual
Machine Translation
Interpretability and Analysis of Models for NLP
Multilingualism and Cross-Lingual NLP
Primary area: social_and_economic_aspects_of_machine_learning
TL;DR: This study makes a key contribution by introducing a novel systematic framework to interpret the translation mechanisms of LLMs from a computational components perspective, an area previously unexplored.

Abstract: While large language models (LLMs) demonstrate remarkable success in multilingual translation, their internal core translation mechanisms, even at the fundamental word level, remain insufficiently understood.
To address this critical gap, this work introduces a systematic framework for interpreting the mechanism behind LLM translation from the perspective of computational components. 
This paper first proposes subspace-intervened path patching for precise, fine-grained causal analysis, enabling the detection of components crucial to translation tasks and subsequently characterizing their behavioral patterns in human-interpretable terms.
Comprehensive experiments reveal that translation is predominantly driven by a sparse subset of components: specialized attention heads serve critical roles in extracting source language, translation indicators, and positional features, which are then integrated and processed by specific multi-layer perceptrons (MLPs) into intermediary English-centric latent representations before ultimately yielding the final translation.
The significance of these findings is underscored by the empirical demonstration that targeted fine-tuning a minimal parameter subset (<5%) enhances translation performance while preserving general capabilities. This result further indicates that these crucial components generalize effectively to sentence-level translation and are instrumental in elucidating more intricate translation tasks.

Exploring the Translation Mechanism of Large Language Models
Hongbin Zhang!? Kehai Chen! Xuefeng Bai!, Xiucheng Li Yang Xiang"*, Min Zhang!
1 Institute of Computing and Intelligence, Harbin Institute of Technoloy, Shenzhen, China Peng Cheng Laboratory, Shenzhen, China
azure. st starzhangggmat com {chenkehat, baizuef eng, liziucheng) @hit. edu. cn, ziangy@pel.. ac. zhangmin2021 dh edu.
Abstract
While large language models (LLMs) demonstrate remarkable success in multilin- gual translation, their internal core translation mechanisms, even at the fundamental word level, remain insufficiently understood. To address this critical gap, this work introduces systematic framework for interpreting the mechanism behind L.M translation from the perspective of computational components. This paper first pro- poses subspace-intervenn enabling the detection components crucial to translation tasks and subsequently characterizing their behavioral patterns in human-interpretabb terms. Compre- hensive experiments reveal that translation predominantly driven by a sparse subset of components: specialized attention heads serve critical roles in extracting source language, translation indicators, and positional features, which are then inte- grated and processed by specific multi-layer perceptrons (MLPs) into intermediary English-centric latent representations before ultimately yielding the final translation.
path patching for precise, fine-grained causal analysis,
The significance of these findings is underscored by the empirical demonstration
that targeted fine-tuning minimal parameter subset (< 5%) enhances translation performance while preserving general capabilities. This result further indicates that these crucial components generalize effectively to sentence-level translation and are instrumental in elucidating more intricate translation tasks. Code is available
this URL
Introduction
Large language models (LLMs) have demonstrated strong capability to handle multilingual translation
tasks (Zhu et al. 2024| Zhang et al.)
2024; Gain et al. (2025), paving the way for a new paradigm in
machine translation (Xu et al. 2024a Alves al. (024)) and progressively approaching human-level translation (Xu et al. al.] 2024c Lu et al. 2024 Xu et al. 2024b). Despite these successes, compre- hensive understanding of the internal core mechanisms underlying LLM translation is still lacking, even for the fundamental word-level translation. This significant gap in interpretability presents considerable challenges in ensuring reliability and further advancement in translation capability. Prior analyses concentrated on surface-level emergent linguistic phenomena (c.g., neuron activation patterns (Mu al. 2024 Tang et al. 2024) or intermediate representations (Wendler al. 2024 Zhu et al. (024)), remaining observational rather than elucidating the computational mechanistic basis underlying translation. A comprehensive understanding of these functional mechanisms is critical for achieving robust improvements in translation capability and advancing the development of controllable and interpretable LLMs (Wang et al. 2203 Zhang et al., 2025).
"Corresoondigg Authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

In this paper, we study the internal mechanism of LLM translation by progressively investigating the following research questions:
Which components of LLMs crucially contribute to performing translation?
What behavioral patterns do these translation-crucia components exhibir?
Can fine-tuning these translation-crucial components enhance LLM translation cupability?
To this end, this paper introduces a systematic framework that, by initially utilizing the proposed subspace-intervene path patching for precise, fine-grained causal analysis, examines the causal con- tributions of computational components to translation, thereby facilitating the detection of components crucial for translation tasks. Subsequently, for components judged as essential, we systematically analyze their behavioral patterns by (1) characterizing attention heads' specialized functional roles according to the attention contribution to lexical alignment and (2) measuring correlations between MLP representations and translation-relevant token embeddings.
Comprehensive analysis indicates that translation predominantly driven by sparse subset of attention heads, which can be characterized into three distinct functional roles: (i) source heads that focus on source-language tokens, (ii) indicator heads that track signals steering the translation task, and (iii) positional heads that maintain sequential coherence. Furthermore, we demonstrate that MLPs iteratively integrate translation-related features from these specialized attention heads, processing them into intermediate, English-centric latent representations.
Building these insights, design targeted optimization strategy to selectively fine-tune translation-crucial components, thereby assessing whether this focused approach improves trans- lation performance. We empirically find that such targeted fine-tuning of minimal parameter subset enhances translation performance while preserving general capabilities, a finding that further underscores the effective generalization of these essential components sentence-level translation.
In summary, our main findings are as follows:
Only sparse subset of heads (less than 5%) are crucial for LLMs' translation.
Crucial heads exhibit specialized functions by processing Prassation-relevan features, which MLPs then integrate and transform into English-cent centric latent representations.
Fine-tuning merely 64 heads achieves performance parity with full-parameter fine-tuning.
2 Related Works
Neural machine translation interpretation. Prior interpretability research in Neural Machine Translation (NMT) (Bau et al.) 2019: Voita et al. (2019) has predominantly focused on sequence-to- sequence (seq2seq) models with encoder-decoder architectures, often analyzing individual attention head contributions via techniques like head pruning. To the best our knowledge, this study is the first to investigate the translation mechanisms underlying decoder-only LLMs. Notably, findings that translation is driven by a sparse subset attention heads and attention heads play specialized functional roles align with previous research (Voita et al. 2019 Behnke and Heafield 2020), suggesting the generalizability of these phenomena across different architectural designs.
Mechanistic interpretability Mechanistic interpretability (MI) elucidates neural network mecha- nisms by seeking to reverse-engineer and decode their functioning (Meng et al., 2022, Lan et al.
2024 Zhao et al. 2024a; Rai et al. 2024). Path patching (Goldowsky-Dii et al.] [0233] Wang et al.
2023 , derived from activation patching (Heimersheim and Nanda 20244] Zhang and Nanda 2024).
probes causal relationships and analyzes interactions between components in neural networks by tracing effect propagation along network pathways via targeted activation interventions. Recent studies highlight the utility of path patching to gain insights into functioning behavior, such as identifying circuits for tasks like indirect object identification (Wang et al. (202)) and arithmetic calculations (Zhang al. 2025). To achieve more precise and fine- ine-ggrined causal analysis, this paper proposes subspace-mterrenee intervened path patching to refine analytical precision and granularity.
Interpretability in multilingual LLMs. Recent studies have delved deeper into how LLMs achieve multilingualism by investigating linguistic phenomena emergent in multilingual context (Bhattachryya and Bojar 2024 Peng and Søgaard 2024 Ferrando and Costa-jussal 2024 Dumas et al. 2024

---

Zaranis et al. 2024). Key findings indicate that (i) increased linguistic diversity in inputs leads
to reduced neuron activations (Mu et al., 2024); (ii) LLMs exhibit language-specitts functional regions (Tang et al. 2024); and (iii) English frequently functions as an implicit computational pivot (Wendler et al., 2024, ZZhao et al. (024b). Unlike prior research centered on surface-level
linguistic phenomena, this work comprehensively analyzes the underlying computational translation mechanisms in LLMs.
3 Constructing Analysis Dataset
To explore I.LM translation mechanisms, we begin with word-level translation, which offers a more tractable, interpretable approach and provides foundational first step understanding core translation processes. Taking inspiration from the prompt design and word selection in Wendler et al.) (2024), we construct a word translation dataset across five widely used languages (e.g.. English (En), Chinese (Zh), Russian (Ru), German (De), and French (Fr)). Taking word translation from English to Chinese (En = Zh) as an example, a word translation prompt containing the translation logic, such as "English: book #x:" " ("#X"" means "Chinese") might appear in the dataset. To eliminate task ambiguity and ensure focused exploration of the translation mechanism, we select the samples that successfully prompting LLMs to translate, as positive data using the notation of X More details of the construction of word translation datasets can be found in Appendix B.
For activation perturbation, we construct a negative dataset comprising counterfactual sentences that exclude translation logic, using the notation of X_ The negative samples are generated adhering to two core principles: (1) preserving grammatical structures from the original X sentences and (2) replacing several crucial words responsible for the translation logic with contextually irrelevant terms. For instance, a sentence from X+ like "English: cloud - -x: is replaced with the corresponding counterfactual one "English: cloud Nothing: This isolates the model's impact on translation tasks from sentence structural or syntactic variables, enabling precise analysis of how LLMs perform translation tasks. The details of multiple counterfactual templates are provided in Appendix B.2
4 Crucial Translation Components Detection
We begin by addressing the first research question: "Which components crucially influence LLMs' translation capabilities?" By leveraging the proposed subspace intervened path patching ($4.1), we detect components crucial for performing translation tasks ($4.2), subsequently validate their impor- tance through knockout ($4.3). and further examine whether these components exhibit consistency across pre-training and post-training phases ($4.4).
4.1 Subspace Intervened Path Patching
Motivated by the linear representation hypothesis that linear subspaces of vectors will be the most interpretable model components (Geiger al. 2024 Makelov al. 2024 Park al. 2024). this paper proposes subspace-intens intervened path patching. This method first identifies "translation cecioo--teenn steering" subspace within component's 's activations using contrastive translation data pairs in an unsupervised manner. Subsequently. interventions are confined to the traaslationnsteering subspace, enabling a precise analysis of the component's causal effect on the final translation.
Identification of translation-steeringg subspace. Building on previous work, which indicates that contrastive pairs optimal choice for extracting desired behaviors from LLMs (Zou al., 2025 Hojer et al. 2025), the proposed method identifies a translation-steering subspace. This is achieved by utilizing translation contrastive activations -ariiioisssseccccaalll..... the difference in activations between input yielding correct translations and those lacking translation logic-to effectively capture the translation signal while excluding homogeneous noise. Formally, for an input sequence 2, an activation vector (x) E Rd is extracted from component at the final token position, where d denotes the component's 's hidden dimension. A curated analysis dataset, comprising N contrastive pairs (x("), x (details in $33. is utilized. For each pair i, the contrastive activation vector A is computed as the difference between the activations from the reference input x and the counterfactual input x!)) To analyze dominant directions of activation shifts in the analysis datasct, these contrastive vectors, {Aa!') form the columns of an activation difference matrix
3

---

M. E RáxN, Inspired by prior research (Xie et al. 2022. Makelov et al. 2024). we hypothesize that M. can be decomposed into two orthogonal subspaces: (i) universal manssaaioo-steering subspace S., representing translation directions shared across word translation datasets, and (ii) specific subspace E., capturing dataset-specific features. Following the methodology of Xie et al. (022):
Piratla et al. (2020), this decomposition is achieved by optimizing the objective:
min IIM S. S.. s.t. Span(S.) 1 Span (Ee),
E ||
(I)
where S. E Rdx11 E, E Rddxr, and r E NR contains the coordinates of the dataset-specific
signals projected onto these components. Algorithm presents the overall procedure to obtain S.
Subspace projection patching. Path patch- ing (Wang et al. 2023 Zhang al. 2025) traces influence from a Sender to a Receiver node. This involves replacing the activation of component e from an original input, a (x(X+), ). with its acti- vation from counterfactual input, a The proposed method refines this by confining the intervention pre-defined, task-steering subspace S. within the activation space of com- ponent c. Formally, let We Rdd be matrix whose columns form an orthonormal basis for S.. The orthogonal projection operator onto this subspace is Ps.- = W. W.",, and the pro- jection onto its orthogonal complement S is Ps. [ Ps. = 7 - W. W.W.". The patched ac- tivation, â., is constructed by combining the pro- jection of the counterfactual activation a (X) onto S. with the projection of the original acti-
Algorithm Task Steering Subspace Identification
Require: Set {(X{), " of N contrastive data pairs, rank specific subspace r.
Ensure: Task-stecring subspace S.
Phase Compute contrastive activations
1: for 4- to N
2: Aac <- a.(( a. (x("))
3: end for
4: M.
Phase 2: Approximation of Mc with rank
5: S. + M
6: E I' + Top- rop-r-SVD (M. -'.''11'
7: M + S' + ET')
Phase 3: Force orthogonal constraint of objective
8: S. < K (M))* 1
9: return S.
vation a (x (8) onto S. (Equation
à Ps. (x(") + Psya W.W. a (x(")) + (1-W. (a.()
(2)
The causal effect mediated by S. along the Sender Receiver path measured by the changes in model output (e.g., the logit of ground-truth tokens), which mitigates confounding effects and yields more precise and interpretable estimates of causal contributions. Algorithm 2 shows the procedure.
4.2 Detection of Translation-Crucia. Components
We then apply the proposed nubspace-intervence path patching to precisely analyze the causal relation- ship between components and translation capability and detect the translation-critical components.
Detection results of crucial heads. This study examines the causal impact on output logits from path patching individual heads across layers in LLaMA2-7B (Touvron et al. 2023). Our analysis focuses two translation directions: Chinese to other languages (Zh = X) and vice-versa (X = Zh Following the criteria of previous work ŞZhang et al. 2025). we define "crucial heads" as those whose magnitude of logit change exceeds 1.0%, threshold empirically determined as most contributions fall within 110%% and consistent with prior studies (Wang et al.. [2023] Heimersheim and Nanda 2024). As depicted in Figure [ where each square position (r. y) denotes the z-th head in the y-th layer, several key findings emerge:
1. Only a sparse subset of heads significantly influences translation performance. For instance, if patching the head at position (8, 31) results in a substantial decrease in the target token's 's logit value, illustrating its critical role in the translation process.
"The details and theoretical justification is are provided in Appendix C
3 Appeedix D.I provides details on standard path patching. while Appendix D.2 presents a comparation. For robustness, we also conduct additional experiments on detecting crucial components in other LLMs and other directions (e.g.. En + X, and X > En). Details are provided in the Appendix G

---

Algorithm 2 Subspace Intervened Path Patching
Require: Set D = {(x x ) of N contrastive data pairs, model M, set of model compo- nents C, set of -tterrng subspace basis matrices (Wa c eeO) C).
Ensure: Node importance scores A = 16e C). & Iterate over each data pair (X x in DD
for < 1 to N do
2: Compute base activations a(X (x and a _(x!"). (4)
3: Yorg )
> Compute original model output (e.g., a specific logit) for x Iterate over each model component
4: for each component C C' do
5:
a t a(x{²)
Initialize the full hybrid activation set with reference activations
6:
ā. + W. a. + (I - WoWT)a > Equation 2 for subspace projection (2) ( ( Vnew
7:
> Compute model output using the hybrid activation set Á Calculate the relative change in output due to patching component e
Nhow
8:
Mong
9: end for
end for
11: for each component E C do
Aggregate the effects for each component across datasets
de < N/4 Σ (0
b> Average the individual effects 6! for component c
13: end for return A
> Return the set of aggregated node/component importance scores
O-
0-
50
5.
5-
5
25
25
10.
10
10
10
10
10
15.
15.
15
15.
margge
15
15-
20
20
20
20
25
-25
25.
-25
25
30
30
30
25 30
10 15
30
10
15
Hoads
MLPs
(a) Zh + En
(b) Zh + Fr
(c) Zh + Ru
50
Change leghts
500
5.
change loght
5.
5-
Shange in leght
10-
10.
10
10
100
25
15
15
change
15
15
e15
20
20
255
-50
-10
50
10 15
10 15
(d) En Zh
(e) Fr Zh
(l) Ru = Zh
Figure Importance of heads related to translation across different directions. Each square at position (z, y) refers to the x-th head in the y-th layer. Red (Brown) squares denote heads (MLPs) that have positive impact on predicting the target token, while grey (purple) squares indicate heads (MLPs) with negative effect. Additional explanations of this figure are available in Apdx. D.3
2. Impactful heads are concentrated in the middle and final layers. Earlier layers lack heads directly influencing target token logits; instead, crucial heads cluster predominantly between layers 12 and 20 and in the final two layers. This pattern remains consistent across all translation directions.
3. Crucial heads exhibit high transferability across translation directions. A notable finding is the significant overlap of crucial heads across diverse language pairs. Analysis reveals that language pairs sharing the same source target language (e.g., En + Zh and Fr Zh) exhibit crucial attention head overlap exceeding 70%, while bidirectional translation pairs (e.g., Fr + En) surpass 60%. This overlap suggests these heads directions. Their consistency across language pairs underscores their importance and transferability, indicating contributions core translation mechanisms regardless of specific languages.
generalizable functions in translation, independent of translation
Detection results of crucial MLPss. Similar to crucial heads, most MI.s in earlier layers (0-14) exhibit negligible influence on output logits, with changes confined to approximately =0.0%. Crucial
5

---

MLPs cluster predominantly after layer 15, exceeding 5.0% logit change, whereas the final layer MLP exhibits a substantial impact-reaching 50.0% on target token logit change. This strong correlation between later MI.P layers and logit changes underscores their critical role in shaping translations.
Extend mechanistic causal analysis to more settings. To validate the generalization of the proposed subspace-intervened path patching, we extend mechanistic causal analysis to three additional settings:
..Lowrrsouree and typologically diverse language pairs (Swahili, Bengali, and Arabic) (Ap- that the sparsity and transfer- ability of crucial attention heads still persist across low-resouree and typologically diverse language pairs, substantiating these characteristics as fundamental translation mechanism of LLMs that are independent of resource availability linguistic typology.
pendix E.T:: p: Results presented in Table and and
2.Sentence-level translation using the WMT23 English-to-Chinese dataset (Koemi et al. 2023) (Appendix F Causal analysis results in Table [12 revealed 46.9% overlap between the top crucial attention heads for word-level and sentence-level translation tasks. Ablation experiments demonstrated that knocking out five shared heads resulted in significant performance degradation for both word-level (-39% in logits) and sentence-level (+36% in PPL) translation tasks, whereas ablating five heads crucial exclusively for sentence-level translation had minimal impact on word-level performance (-2%) but caused substantial degradation in sentence-
translation (+43% in PPL), highlighting the functional specialization of attention heads for cetence-level translation.
3. Multilinguul mathematical reasoning using MGSM (Shi et al. 2023) (Appendix F.3 key
strength of the proposed subspace-in innteree-tte path patching is its task task-agnostic ability to generalize across different tasks without requiring task-specific modifications. We then extend the mechanistic causal analysis to the multilingual mathematical reasoning task. We generated counterfactual exam- ples by altering mathematical instructions while preserving the core mathematical content, following the procedure outlined in Section 3 Some examples are listed in Table [3] Our analysis revealed sparse set of critical attention heads for mathematical reasoning, comprising only 3.95% of all heads in the model. This sparsity pattern aligns with our findings regarding the translation mechanism, demonstrating consistency across different cognitive tasks. Ablating the top- 10 critical heads caused 60% drop in reasoning accuracy, confirming their mathematical reasoning functional importance.
4.3 Validating Crucial Components Through Knockout
Interpretive analyses of model components risk
100
misleading or non-rigorous (Bolukbasi et al.)
Translation Accuracy (9))
2021 Wiegreffe and Pinter 2019). To ensure re- liability, we validate the significance of detected crucial components via mean ablation (Wang et al., (023). This method replaces a compo- nent's activation with average activations across counterfactual data X_, effectively neutralizing its task-specific information. Performance de- cline confirms component's importance for translation tasks, whereas no significant perfor- mance change suggests it is not critical.
80
60
40
A
Key Heads
20
*
Random Heads
Key MLPs
Random MLPs
0
1
2
3
4
5
6
7
8
9
10
Validation results the analysis dataset. We examine how incrementally knocking out En + Zh crucial heads affects LLM translation perfor- mance on the analysis d []
Component Knockouts
Figure 2: Translation accuracy changes when com- ponents are progressively knocked out.
As shown in Figure 2 disabling "crucial heads" leads to a significant decline translation accuracy, whereas knocking out "random heads" causes minor fluctuations, with accuracy remaining stable within 2%. similar trend can be observed when knocking out MLPs. These results highlight the essential role of the detected key attention heads in sustaining the translation capability of LLM.
4.4 Examine Consistency of Crucial Components Across Training
We have also conducted validation experiments randomly selected datasets, see Appendix H

---

To investigate whether crucial at- tention heads remain consistent across distinct training phases, we analyze (1) continued pre-
0
50
5-
Sonne in logits
10-
15.
-0
-0
20.
25.
20
25
25:
20-
25
training (CPT) (Xu et 2024a)
30
30
on the LLaMA-2-4 base model I billion tokens of OSCAR data (Ortiz Suárez et al. 2020) and (2) supervised fine-tuning (SFT) (Jiao et al. 2023) on LLaMA-2-7B base model on the WMT17-22 validation dataset.
5 10 20 30
10 15 20 25 30
Attention Heads
MIPs
Attention Hoads
MLPs
(a) CPT-LLM
(b) SFT-LLM
Figure 3: Importance of components related to En Zh transla-
tion across ILaMM-2-7B after CPT or SFT.
Detection results. As illustrated in Figure 3 compared to the base LLM results in Fig- ure ld LLMs after CPT exhibit significant distributional shifts translation-crucial heads, whereas changes minimal after SFT. This finding is statistically supported by our Two-Sampp test on overall logit change distributions (Table TT. which revealed that CPT induces a significant distributional shift (p 0.00001) within the top 32 attention heads, while SFT does not (p 0.355).
Table 1: Statistical comparison of logit changes between base model and trained models
Comparison
K-S Test p-value
Changed Heads
Max Ampie
Base vs. SFT
0.355
of
3.12
Base CPT
0.00001
of
12.03
Satnogorrv--mmimoo
Discussion of the emergence translation capability. A comparative causal analysis incorpo- rating a randomly initialized baseline further elucidates these findings. The randomly initialized model exhibited no specialized translation heads, whereas the base pre-trained model developed critical translation heads with statistically significant distributional shift from the random baseline. In contrast, the SFT model showed only a minor, non-significant distributional shift relative to the pre-trained model. These results demonstrate that the pre-training stage fundamentally alters LLMs' translation capabilities, while supervised fine-tuning primarily focuses on localized parameter adjustments without modifying core abilities. Additional details provided in Appendix
5 Behavioral Patterns Analysis
Motivated by the sparse distribution crucial heads, we now turn to the second research question: "What behavioral patterns do translation-crucial components exhibit?" by systematically investigating their computational mechanisms through two interpretable diagnostic methods: (1) visualizing attention patterns to characterize the roles of crucial heads (Section $..1). and (2) projecting MLP representation to measure correlations with cranslation-relatee token embeddings (Section $5.2).
5.1 Analysis of Attention Head
Acknowledging that attention weights alone may not fully explain model behavior (Kobayahhi et al. 2020), this study investigates attention outputs analyze significant token interactions during translation. Formally, for each analyzed head (i,j), its weighted value output, O6.5) NxN, is defined as Equation
O (+.) ||A (6:) (1 (w I|e,
where N represents the sequence length, A RNNN contains the attention weights, N Xdmaas the input sequence representation, w(()) E Rdmmmnddes is the value weight matrix, and dmodet: dhead are the hidden dimension of model and head respectively. The role of each head is then determined by analyzing the salient features of O (KJ) END. RINN, which represents the interaction between the Query token at the END position and all Key tokens.
Characterizing heads. We first gain an intuitive insight into the "behavioral pattern" of the translation-crucial heads by visualizing attention values as shown in the case in Figure 4 Building on the distinct focus patterns these heads exhibit across different input token types, and following Voita et al. 2 we further categorize them into three distinct functional roles (illustrative examples are provided in Appendix
FFocus Zh + En, with more directions results seen Appendix J
7

---

1) Source Heads demonstrate concentrated attention source-language tokens, specializing in cross-lingual alignment. These heads facilitate lexical transfer by identifying source language tokens among the input sequence.
2) Indicator Heads exhibit spike-shaped attention patterns translation-specific indicators (e.g., language identifiers like "English" or " and structural cues like colons), assisting translation mode recognition and syntactic boundary detection.
3) Positional Heads predominantly attend to adjacent tokens, managing contextual dependencies and resolving grammatical agreement.
Muchtm Rouls QUIBLES
Roudd
Altotton Karka
Baglab
Dagiab
Engis
(a) Source Head (18, 17) (b) Positional Head (4, 31) (c) Indicator Head (27, 14) (d) Indicator Head (4, 14)
Figure 4: The attention values visualization of the oole-classified key heads in Zh = En, which show different characteristies of different crucial heads.
Nemalnnd Atenion Valee
0.5
Sprte
Somaline Atttionn Viter
0.5
Sparee
Distinct attention distribution across heads. To quantitatively analyze the distinct patterns of heads' roles, we plot the distribution of their at- tention values on 100 randomly selected samples for Zh < En translation tasks" Figure 5 demon- strates that source heads predominantly focus on source tokens, positional heads distribute at- tention uniformly across the input context, and indicator heads concentrate on translation task indicator tokens, with all types showing minimal attention to irrelevant tokens.
Lidrorox
0.4
0.2
0.3
Eagbob
(a) Zh En
(b) En
Zh
Figure 5: Mean and standard deviation of attention
values from key head roles across input tokens.
5.2 Analysis of MLP
This study analyzes the linguistic content encoded in the inputs (MLP,)) and outputs (MLPoud of MLP layers, focusing on translation-sted steering tokens: the translation indicator (IND), source language (SRC), and target language (TGT). To achieve this, we utilize the unembedding matrix Wo E Rd X (i.e., the final linear layer that projects hidden states of dimension dmodal onto the vocabulary space size [V]) as a diagnostic probe, where Wo [TOK] denotes the unembedding vector corresponding to specific token TOK. To quantify linguistic information propagation through MLP layers, we compute cosine similarities, denoted as (MLP, TOK), between Wo [TO) and both MLP.,. and MLPout- Furthermore, follow Geva et al. (2022) by evaluating the cosine similarity of the layer's 's normalized change vector (MLPout out MLP... ...) with the normalized token embedding, as defined in Equation 4
isolate the MLP layer's specific contribution,
(M MLP TOK) = MLPout MLP
[TK] TOK
(4)
|L MLP.,
W [[K]]
MIPss iteratively process translation-relatee features to generate target translations. Analysis of MLP interactions with source and target tokens in 100 En = Zh samples (Figure reveals distinct operational phases across layers. Initially (layers 1-14), Figure 6a shows (MLP,,, ........SC SRC) values remain pear-zero, indicating minimal source token encoding, consistent with the inactive region before layer 14 (Figure [d]. significant increase in LM SRC) occurs between layers 15-25, correlating with the actvation of key attention heads, as source information is encoded in MLP representation. Subsequently, from layers 25-31, (M SRC) decreases, signaling transition towards target translation. Concurrently, ((ML,,.. in, IND)) begin to rise after layer 12, peaking in
Statistical significance analysis is available in Appendix

---

the final layers to facilitate coherent target- langet-languaee generation. Control comparisons using random English tokens (.. RAND)) consistently remain near-zero, confirming the observed pattern's specificity. Furthermore, Figure 6b demonstrates that from layer 15, where MLPs begin processing target token information, [MI.Pau out - ML.P.. Wu (TARGETT) progressively increases, suggesting the generation of translation. The phenomenon is generalizable as evidenced by similar trends in other LLMs (Appendix
MLP intermediate features reveal an English-centric latent representation as a translational intermediary. Further investigation into the correlation between MLP intermediate representations and the unembedding vector of semantically equivalent tokens across different languages during non-English translation pairs (e.g., De/Ru + Zh) yields a significant finding. As illustrated in Figure / the similarity of these intermediate representations to English unembedding vectors is markedly higher in layers 16-26 compared to other languages, subsequently declining layers 25-31. mechanism. In this process, source inputs appear to processed into an English-centric latent space before generating target language outputs, analogous to humans using their native language as a mental intermediary. This observation corroborates prior research (Wendler et al. 2024, Zhao et al. 2024b), affirming the pivotal role of English as a latent intermediary in multilinuull LLM operations.
This pattern strongly suggests that LLM employs a "bridge-translatiin
0.25
325-
Caeine Sinilatty
0.70
(MLP... (SRC))
(RAND))
3.20
MMP
MLP (TOT)
(RAND))
(ML (IND)
0.10
2.10-
0.05
2.05
36"
(a) De + Zh
(b) Ru = Zh
(a) Reception Trend
(b) Generation Trend
Figure 7: The correlation between the MLP
Figure 6: The correlation between MLP input or representation and the language's unembedding output with cransltion-related tokens.
vector.
More discussions on English as the pivot language. Appendix M. presents a correlation analysis demonstrating the direct and significant impact of English-centricity on translation performance. We then explore different aspects of English
pivot language: Appendix MM.2 examines why English emerges as the pivot, while Appendix MM3 annaazze the role of English in forming the forward process of language models. Finally, Appendix M.44 investigates potential gender/formality translation biases introduced by English as a pivot latent representation.
Targeted Enhancement of Translation Capability
Building on the insights from two previous investigations, we aim to answer the final question: "Can fine-tuming these translation-crucial components enhance LLM translation capability?"" We initiate by introducing our comparative experimental setup and results (Section $6.1) and further carry out two sets of analysis experiments (Section 6.2 and 6.3)
6.1 Experimental Setup and Results
Experimental setup. We examine three approaches on Zh < En and De <> En directions: (1) full-parameter fine-tuning (Full SFT), (2) the proposed selectively fine-tuning of translation-crucial components (Targeted SFT), and (3) random-componen fine-tuning (Random SFT), where random components match the parameter count of Targeted SFT. For training, we leverage human- -araalla corpora WWMT17-WMT22
Flores-200 (Guzmán et al. 2019) following Xu al. (2024a), evaluating translation accuracy on WMT23/24 and coneral-domant benchmarks (MMLU (Hendrycks et al.
2021b, ARC (Clark et al. 2018 SIQA (Sap et al. 2019). More details are provided in Appendix K
Experimental results. Tables 2
highlight three key advantages of Targeted SFT: (1) Improved
Translation Performance: Targeted SFT significantly enhances translation performance across all language directions, surpassing Full SFT and substantially outperforming Random SFT. (2)
Preservation of General Capabilities: Unlike Full SFT, which degrades performance on non- translation tasks, Targeted SFT maintains baseline general capabilities. (3) Enhanced Training Efficiency: It modifies fewer than 5% of parameters and reduces training time by half compared

---

Table 2: The overall evaluation results on Zh + En, De En translation tasks and generic tasks.
Translation Tasks
Generie Tasks
Models
Train Speed
Tuned Params,
7h
En->Zh
De
En
MMLU
Commonsense Reasoning
BI BLEUSYCOMETT//BBUUUE
Acc.
Acc.
LLaMA2-7B
15.6/73 1/56.6
17.0/74.155.99
24.8/76.8/6..1
13.0/64.249) 1
45.9
55.3
Full
1ssmm/sec.
20.4/78. 766399
30.3/80.7/62.9
35.4/834470.7
27.9/78.363.7
Targeted SFT
//sam/soc.
0.27B
21,3/79. 1/64,3
30.7/8144/64.3
37.1/83.1771.4
27.6/78.4/66
46,0
55.7
Random SFT
33sam//ccc.
0.278
16.9/76.9/1.11
26.4/79./6116
32.5/81.6/68.1
22.7/76.2/00.
45.9
54.9
to Full SFT. Furthermore, these positive results demonstrate that the detected translation-crucia heads generalize beyond isolated word translation and are significant to sentence-level translation. Additional results on more directions and LLMS are provided in Appendix L
6.2 Language Transfer Evaluation of Crucial Translation Heads
This section evaluates the language transfer ca- pabilities of crucial translation heads. Specifi- cally, heads detected crucial for En Zh trans- lation were selected for fine-tuning and evaluat- ing on En + Ja/Cs translation tasks. The com- parable results, shown in Table 3 indicate that these translation-erucial attention heads exhibit cross-lingual generalization.
Table 3: The transfer evaluation results of En + Zh erucial heads on En Cs and En Ja.
Models
Ea
EamJa Jan
Cs-Fia
Fnn
BLEUNCOMET MBLLUUTT (BBLEUTT
LLaMA2-78
4.49636//997
6.1/73.34474
23.3777.906.1
16.872
Full SFT
30 00.2/50.0/66 0/66
15.2/2 4/56.7
31.883.1/11
17.4/79 5A64
Torgetad SFT
30.8/90 3/667
15.381 96567
33.58335572.3
18.7/90-194..
Random SPT
15.8/8855633.8
9/5377
29.18815588.6
14.0/7.9962.1
Table 4: Ablative experiments attention heads.
Table 5: Ablative experiments on MLPs.
Albltting Atteation Heads
Train Speed
Toned Params.
7h = Ea
MMLEU
Ablating MLP'
Train Speed
Tuned Purams.
2h En
MMLU
BLKU/KIMEE//LLURTT
Acc.
BI BLEUSCOMETALLUU
Avc.
txp-8 heads
SasumnAace
7.
18.7/78 16330
46.1
heads
33sam Sec.
0.27
21.3/7% 1/64.3
1--16 boads
Sesam/Aace.
8.0338
30.1/TXAN6..5
45.9
2xp-32 heads
BL067B
30.4/7X66633 6663.
45.8
+top-1 ML.P
10sam Aec.
21,879 1/64.5
45.7
top- 64 heads
40sum/sec..
0.134B
21.3779.1N6413
45.9
+tp-2 MLP
Avc.
0.549
U864
lep-06 howds
Masmmeec
0.1 348
21,079 6W641
45.7
+tp-3 ML.
Avc.
21,0/70 1864
45.3
11g-128 hrauls.
0.3688
21.1/70 184.4
45.5
+tpp
bec.
22.1/79.2/64.6
1ep-160 heaads.
SOssanAace:
0.3358
21.379)11644
45.3
421l MLP
18kams Noc.
22.57794/64.7
6.3 Ablation Study of Trainable Components
Ablation studies on Zh + En translation were conducted to assess the impact of varying the number of fine-tuned attention heads and MLPs on translation performance, generic capabilities, and training efficiency. As indicated in Table increasing the quantity of fine-tunable attention heads enhanced translation performance but concurrently weakened generic capabilities. Notably, fine-tuning 64 attention heads achieved an optimal balance between performance and computational cost. Further- more. Table reveals that while augmenting the number of MLPs improved translation performance, this approach more substantially degraded generic capabilities and reduced training speed compared to the fine-tuning of additional attention heads.
6.4 Supplementary Experiments
This section presents three additional experiments of targeted SFT: (1) evaluation results on domain- adaptive translation (Appendix [. (2) analysis of potential cultural bias amplification (Ap- pendix N.2). and (3) qualitative case studies examining characteristic patterns (Appendix N.3
Conclusion
This study systematically explores the translation mechanisms of LLMs by progressively addressing three research questions. We first identify components crucial for translation using our proposed subspace-interveneed path patching, revealing that only a sparse subset of components (less than 5%) are indispensable. These heads exhibit specialized functions, extracting conslatiin-relatee features, while MLPs integrate and process information towards intermediate, English-centrie latent representations. Based on these findings, we empirically demonstrate that targeted fine-tuning of merely 64 translation-crucial heads achieves performance parity with full-parameter tuning. These results further emphasize the effectiveness of generalizing the detected crucial components to sentence-level translation. This work serves as a preliminary exploration the translation mechanism underlying LLMs, establishing solid foundation for clucidating more intricate translation tasks.
10

---

Acknowledgment
We express our sincere gratitude to the reviewers for their valuable and insightful comments, which have significantly improved the quality of this work. This work was supported in part by the National Natural Science Foundation of China under Grant 62276077, Grant 62406091, and Grant U24A20328, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A151501 1205, in part by the Shenzhen College Stability Support Plan under Grant GXWD20231 1 130104007001, in part by the Major Key Project of PCL under Grant PCL2025A12, and in part by the Shenzhen Science and Technology Program under Grant KQTDD024079102155466 and Grant Z2SYS2023066609122000
References
Duarte M. Alves, José Pombal, Nuno M. Guerreiro, Pedro H. Martins, João Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, Pierre Colombo, José G. C. de Souza, and André F. T. Martins. 2024. Tower: An open multilingual large language model for translation- related tasks Preprint, 17211:2402.17733.
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass. 2019.
Identifying and controlling important neurons in neural machine translation In International Conference on Learning Representations.
Maximiliana Behnke and Kenneth Heafield. 2020. Losing heads the lottery: Pruning transformer attention in neural machine translation] In Proceedings of the 2020 Conference Empirical Methods in Natural Language Processing (EMNLP), pages 2664-2674, Online, Association for Computational Linguistics.
Sunit Bhattacharya and Ondřej Bojar. 2024. Understanding the role of fins in driving multilingual behaviour 11ms] Preprint, arXiv:2404.1333 13855..
Tolga Bolukbasi, Adam Pearce, Ann Yuan, Andy Coenen, Emily Reif, Fernanda Viégas, and Martin Wattenberg. 2021. An interpretability illusion for bert Preprint, arXiv:2104.07145.
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv: N00::800330000 05457vl.
Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas, Saloni Potdar, and Yunyao Li. 2024. Towards cross-cultural machine translation with retrieval -ugmente generation from multilingual knowledge graphs] In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16343-16360, Miami, Florida, USA. Association for Computational Linguisties.
YITian Ding, Jinman Zhao, Chen Jia, Yining Wang, Zifan Qian, Weizhe Chen, and Xingyu Yue. 2025. Gender bias in large language models across multiple languages: A case study of ChatGPT In Proceedings of the 5th Workshop Trustworthy NLP (TrustNLP 2025), pages 552-579, Albuquerque, New Mexico. Association for Computational Linguistics.
Clément Dumas, Veniamin Veselovsky, Giovanni Monea, Robert West, and Chris Wendler. 2024. How do Ilamas process multilingual text? a latent exploration through activation patching In TCML 2024 Workshop on Mechanistic Interpretability.
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatficld-Dodds Danny Hemanndez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2021. A mathematical framework for transformer cireuits. Transformer Circuits Thread. ffap:ereiciicuuuuooe/aaaaaaaaao
Javier Ferrando and Marta R. Costa-jussà. 2024. On the similarity of circuits across languages: case study on the subject-verb agreement task In Findings of the Association for Computational Lin- guistics: EMNLP 2024, pages 10115-10125, Miami, Florida, USA. Association for Computational Linguistics.
11

---

Baban Gain, Dibyanayan Bandyopadhyay, and Asif Ekbal. 2025. Bridging the linguistic divide: A sur- vey on leveraging large language models for machine translation, arXiv preprint arXlv:2504.0191.
Atticus Geiger, Zhengxuan Wu, Christopher Potts. Thomas Ieard. and Noah Goodman. 2024. Finding alignments between interpretable causal variables and distributed neural representations In Pro- ceedings of the Third Conference on Causal Learning and Reasoning, volume 236 of Proceedings of Machine Learning Research, pages 160-187. PMLR.
Mor Geva, Avi Caciularu, Kevin Wang, and Yoav Goldberg. 2022. Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space, In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 30-45, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Nicholas Goldowsky-Dill Chris MacLeod, Lucas Sato, and Aryaman Arora. 2023. Localizing model behavior with path patching. arXiv preprint arXiv:2304.05965
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishray Chaudhary, and Marc' Aurelio Ranzato. 2019. The FLORES evaluation datasets for low-resource machine translation: Nepali-English and Sinhala- Englishh In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCLLP), pages 6098-6111, Hong Kong, China. Association for Computational Linguistics.
Stefan Heimersheim and Neel Nanda. 2024. How use and interpret activation patching Preprint, M.Kiv:2404.15255
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazcika, Dawn Song, and Ja- cob Steinhardt. 2021. Measuring massive multitask language understanding In International Conference on Learning Representations.
Bertram Højer, Oliver Simon Jarvis, and Stefan Heinrich. 2025. Improving reasoning performance in large language models via representation engineering In The Thirteenth International Conference on Learning Representations.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023. Mistral 7b Preprint, arXiv:2310.06225
Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang. Shuming Shi, and Zhaopeng Tu. 2023, ParroT: Translating during chat using large language models tuned with human translation and feedback, In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1509-15520,, Singapore. Association for Computational Linguistics.
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, and Kentaro Inui. 2020. Attention not only a weight: Analyzing transformers with vector norms In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7057-7075, Online. Association for Computational Linguistics.
Tom Koemi, Eleftherios Avramidis, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Markus Freitag, Thamme Gowda, Roman Grundkiewicz, Barry Haddow, Philipp Koehn, Benjamin Marie, Christof Monz, Makoto Morishita, Kenton Murray, Masaaki Nagata, Toshiaki Nakazawa, Martin Popel, Maja Popović, Mariya Shmatova, and Jun Suzuki. 2023. Findings of the 2023 conference on machine translation (WMT23): LLMs are here but not quite there yet In Proceedings of the Eighth Conference on Muchine Trunslation, pages 1-42, Singapore. Association for Computational Linguistics.
Michael Lan, Philip Torr, Austin Meek, Ashkan Khakzar, David Krueger, and Fazl Barez. 2024. Sparse autoencoders reveal universal feature spaces across large language models. Preprint, arXiv:2410.06981.
12

---

Yinquan Lu, Wenhao Zhu, Lei Li, Yu Qiao, and Fei Yuan. 2024. LLaMAX: Scaling linguistic horizons of LLM by enhancing translation capabilities beyond 100 languages In Findings of the Association for Computational Linguistics: EMNIP 2024, pages 10748- Miami, Florida,
USA. Association for Computational Linguisties.
Alcksandar Makelov, Georg Lange. Atticus Geiger, and Neel Nanda. 2024. Is this the subspace you are looking for? an interpretability illusion for subspace activation patching In The Twelfth International Conference Learning Representations.
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022. Locating and editing factual associations in gpt In Advances in Neural Information Processing Systems, volume 35, pages 17359-17372. Curran Associates, Inc.
Yongyu Mu, Peinan Feng. Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, and JingBo Zhu. 2024. Revealing the parallel multilinuul learning within large language models] In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6997, Miami, Florida, USA. Association for Computational Linguistics.
Pedro Javier Ortiz Suárez, Laurent Romary, and Benoît Sagot. 2020. A monolingual approach to contextualized word embeddings for mid resource languages In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1703-1714, Online. Association for Computational Linguistics.
Kiho Park, Yo Joong Choe, and Vietor Veitch. 2024. The linear representation hypothesis and the geometry of large language models. In International Conference on Machine Learning, pages 39643-39666. PMLR.
Qiwei Peng and Anders Søgaard. 2024. Concept space alignment in multilingual LLMs. In Pro- ceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5511-5526,, Miami, Florida, USA. Association for Computational Linguistics.
Vihari Piratla, Pranceth Netrapalli, and Sunita Sarawagi. 2020. Efficient domain generalization via common-specific low-rank decomposition. In International conference machine learning, pages PMLR.
Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, and Ziyu Yao. 2024. A practical review of mechanistic interpretability for transformer-based language models Preprint, atXiv:2407.02641
Maarten Sap, Hannah Rashkin, Derek Chen. Ronan Le Bras, and Yejin Choi. 2019. Social IQa
Commonsense reasoning about social interactions) In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference Hong Kong, China.
on Natural Language Processing (EMNLP-IJCNIP), pages 46334473,, Association for Computational Linguistics.
Beatrice Savoldi, Sara Papi. Matteo Negri, Ana Guerberof-Arenat and Luisa Bentivogli. 2024. What the harm? quantifying the tangible impact of gender bias in machine translation with a human-centered study In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18048-18076 Miami, Florida, USA. Association for Computational Linguistics.
Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder. Denny Zhou, Dipanjan Das, and Jason Wei. 2023.
Language models are multilingual cf reasoners In The Eleventh International Conference on Learning Representations.
Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang. Xin Zhao, Furu Wei, and Ji-Rong Wen. 2024. Language-speciffcc neurons: The key to multilingual capabilities in large language models) In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume Long Papers), pages 5701-5715, Bangkok, Thailand. Association for Computational Linguistics.
13

---

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019. Analyzing multi-head Self-attention: Specialized heads do the heavy lifting, the rest can be pruned In Proceedings of the 57th Anmual Meeting of the Association for Computational Linguistics, pages 5797-5808, Florence, Italy, Association for Computational Linguistics.
Kevin Ro Wang, Alexandre Variengien, Arthur Commy, Buck Shlegeris, and Jacob Steinhardt. 2023. Interpretability in the wild: a circuit for indirect object identification in GPT-2 small In The Eleventh International Conference Learning Representations.
Chris Wendler, Veniamin Veselovsky, Giovanni Monea, and Robert West. 2024. Do llamas work in English? on the latent language of multilingual transformers) In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15366-15394, Bangkok, Thailand. Association for Computational Linguistics.
Sarah Wiegreffe and Yuval Pinter. 2019. Attention is not not explanation, In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNL.)IJCNLL)), pages 11-20, Hong Kong, China. Association for Computational Linguistics.
Zhihui Xie, Handong Zhao, Tong Yu, and Shuai Li. 2022. Discovering low-rank subspaces for language-agnostic multilingual representations, In Proceedings of the 2022 Conference on Em- pirical Methods in Natural Language Processing, pages 5617-5633, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Haoran Xu, Young Jin Kim, Amr Sharaf, and Hany Hassan Awadalla. 2024a. A paradigm shift in machine translation: Boosting translation performance of large language models) In The Twelfth International Conference Learning Representations.
Haoran Xu, Kenton Murray, Philipp Koehn, Hieu Hoang, Akiko Eriguchi, and Huda Khayrallah. 2024b. X-alma: Plug & play modules and adaptive rejection for quality translation at scale Preprint, arXiv:2410.03115.
Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. 2024c. Contrastive preference optimization: Pushing the boundaries of LLM performance in machine translation. In Forty-first International Conference on Machine Learning.
Qwen An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxin Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yi-Chao Zhang, Yunyang Wan. Yugi Liu. Zeyu Cui, Zhenru Zhang. Zihan Qiu, Shanghaoran Quan, and Zekun Wang. 2024. Qwen2.5 technical report, ArXiv, abs/2412.15115.
Binwei Yao, Ming Jiang, Tara Bobinac, Diyi Yang, and Junjie Hu. 2024. [Benchmarking machine translation with cultural awareness) In Findings of the Association for Computational Linguis- lies: EMNLP 2024, pages 13078-13096, Miami, Florida, USA. Association for Computational Linguistics.
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. 2025. Language models super mario: absorbing abilities from homologous models as a free lunch. In Proceedings of the 41st International Conference Machine Learning, ICML'24, JMLR.org.
Emmanouil Zaranis, Nuno M Guerreiro, and Andre Martins. 2024. Analyzing context contributions in LLM-based machine translation In Findings of the Association for Computational Linguis- fies: EMNLP 2024, pages 14899-14924, Miami, Florida, USA. Association for Computational Linguistics.
14

---

Fred Zhang and Neel Nanda. 2024. Towards best practices of activation patching in language models; Metrics and methods In The Twelfth International Conference on Learning Representations.
Hongbin Zhang, Kehai Chen, Xuefeng Bai, Yang Xiang, and Min Zhang. 2024, Paying more attention to source context: Mitigating unfaithful translations from large language model In Findings of the Association for Computational Linguistics: ACL 2024, pages 13816 18886-13836 13836, Bangkok, Thailand. Association for Computational Linguistics.
Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-ming Cheung, Xinmei Tian, Xu Shen, and Jieping Ye. 2025. Interpreting and improving large language models in arithmetic calculation. In Proceedings of the 41st International Conference Machine Learning, ICML'24. JMLR.org.
Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai. Shuaiqiang Wang, Dawei Yin, and Mengnan Du 2024a. Explainabilityy for large language models: A survey ACM Trans. Intell. Syst. Technol., 15(2).
Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, and Lidong Bing. 2024b. How do large language models handle multilingualism? In The Thirty-eighth Annual Conference Neural Information Processing Systems.
Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. 2024. Multilingual machine translation with large language models: Empirical results and analysis) In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2765 2781, Mexico City, Mexico. Association for Computational Linguisties.
Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson. J. Zico Kolter, and Dan Hendrycks. 2025, Representation engineering: A top-down approach to ai transparency Preprint, arXiv:2310.01405
15

---

A Limitations and Discussion
Limitations. This study acknowledges a methodological consideration that guides future research directions. Although our parameter-aware methodology proves effective across open-source architec- tures, its applicability to closed-soure systems remains theoretically constrained limitation that simultaneously highlights the urgent need for developing -odel-agmostic analysis frameworks in this evolving research domain.
Potential impact. This study pioneers the exploration of translation mechanisms fine-grained level by directly investigating the causal relationship between model components and translation performance. The employed interpretability techniques, such attention visualization, distribution analysis, and unembedding quantification, are generalizable and can be extended to future research questions in interpretable machine learning. Furthermore, the systematic interpretability methodology presented is adaptable to other Natural Language Processing (NLP) tasks (e.g., summarization, question answering) and potentially to non-NLP domains, thereby encouraging further investigation into task-specific component analysis. The identification of universal translation components across diverse language pairs can inform the development of more robust multilingual Large Language Models (LLMs), particularly benefiting low-resource languages.
Practical applications. Practical applications of this study stemming from these insights signifi- cant. Targeted fine-tuning, guided by the identification of key components, promises considerable computational efficiency. Specifically, the findings suggest that fine-tuning only essential components, rather than retraining entire models, can significantly reduce computational costs while preserving translation quality. Moreover, this research contributes interpretable Artificial Intelligence (AI) for translation by offering transparent, component-level understanding of how translation decisions are formulated. Such transparency crucial for fostering trust and facilitating adoption in critical real-world scenarios, including legal, medical, and diplomatic applications.
Future research. Future research directions also illuminated by this work. While the current analysis concentrated on word-level translation to isolate core mechanisms, subsequent studies could extend these insights to sentence-level and document-level contexts achieve understanding, Additionally, although this study focuses on specific components, the principles and findings can inform the design and analysis of larger and more complex models. As LLMs continue to increase in scale and complexity, a thorough understanding of their internal mechanisms becomes increasingly essential, and this work provides foundational basis for such endeavors.
comprehensive
B Translation Task Templates and Examples
As clear case study, we first focus Chinese due its prevalence of single-token words and lack of spacing. We analyze Llama-2's vocabulary to identify single-token Chinese words (primarily nouns) with direct single-token English translations. This enables direct comparison of the model's next-token probabilities for correct Chinese words and their English equivalents. For robustness, replicate experiments in German, Russian, and French, compiling datasets of 139 Chinese, 120 German, 15 Russian, and 1 118 French words.
B.1 Dataset Construction
To ensure the next token is unambiguously inferable as single token, we design translation prompts where 1n+1 is uniquely determined by the preceding context II...I,. Each prompt specifies the source language. word, and target language, requiring the model to predict the translated word. Taking English-to-Chinese as an example, a word translation like "English: flower 4 X: 7E" (" means "Chinese", "ZE" means "flower") might naturally appear in the pretraining corpus. Such prompts explicitly guide Llama-2 to perform translation by leveraging its pretrained linguistic knowledge.
B.2 Templates
We formalize counterfactual prompt generation through systematic grammatical preservation and semantic disruption, operating under two core design principles:
16

---

Structural Isomorphism:
Maintain original syntactic patterns (interrogative formats, place- holder positions, punctuation) while altering semantic content
Targeted Lexical Substitution:
Replace critical components through four operation classes
Perturbation Taxonomy in Table
The perturbation strategies fall into four principal categories, as detailed
Table 6: Taxonomy of Counterfactual Perturbation Operations
Operation Type
Implementation Mechanism
Target Nullification
Replace language identifiers with non-linguistic concepts (ftgt_lang) - "Void"/"Null")
Action Distortion
Substitute translation verbs with irrelevant actions ("trans- late" - "deltee"
Semantic Obfuscation
Alter task-specific nouns to disrupt functionality ("transla- tion" " + "coolor/'flavor")
Paradox Insertion
Introduce self-contradictory modifiers (tgt_lang)" + "into a silent rock")
("into
Validation Protocol
The constructed templates undergo rigorous verification:
1. Grammatical Integrity Check: Measure template fluency via language model perplexity scores (threshold: 155% deviation from originals)
2. Task Disruption Test: Verify semantic shift through human annotation (success criterion: 290% agreement on functionality removal)
The counterfactual prompts we used are shown in Table
Table 7: Examples of some regular translation prompt templates and counterfactual prompt templates.
Normal Prompt
Counterfactual Prompt
Perturbation Type
(arc_ lang]]ang): "[arc_vord}"
(erc_lang)) "(arc_vord}" There is nothing: "[tgt_word)
Target Nullification
(tgt_langh: "(tgt_ Translate "(src_word)" into (tgt_lanng):
Translate "[src_word}" into Nothing:
Target Nullification
Translate the lare_lang) word "(urc_word]" to (tgt_lang): From (sre_lang): "(sre_vord)" to (tgt_lang):
Translate the (arc_lang) word "(src_word)" to Null:
Target Nullification
Fron (src_lang): "[src_word)" to Nowhere:
Target Nullification
Provide the translation of "[src_word)" (ere_lang) (tgt_lang):
Provide the color of (src_vord)" (arc_lang) (tgt_lang):
Action Distortion
Q: How do you say "[ere_word]" in (tgt_lang)? A: Q: What is the (tgt_lang) translation "[src_vord]"? A: Translate "[src_word)" into (tgt_lang),
Q: How do you eat "(ere_word)" in (tgt_lang)? A: Q: What is the (tgt_lang) flavor "[sre_word)"? A:
Action Distortion
Semantic Obfusca- tion
Translate "[src_word}" into silent rock:
Paradox Insertion
Q: What 1s "ferc_word]" translated (tgt_lang)? From (arc_lang): "(are_vord}" (tgt_lang): "(rgt_vord)
Q: What "ferc_word}" erased into [tgt_lang]?
Action Distortion
From {arc_lang): "[are_vord}" Disabled: "(tgt_vord)
Action Distortion
Note: All placeholders ([src janglann,, (sre_ word], etc.) follow actual implementation syntax. Counterfactual perturbations preserve original grammatical structures while altering translation semanties through targeted substitutions.
17

---

Evidence supporting the choice of the contrastive template. To further substantiate this choice, we present two key evidences of why this contrastive template suitable:
Empirical Validation: Applying the contrastive template consistently results in 0% accu- racy, confirming that the template reliably triggers L.MM not to perform translations.
Reference to Prior Work: We drew inspiration from Wang al. 2023), where manually created contrastive samples were used for the Indirect Object Identification (IOI) task. For example:
Original prompt: The store Cody and Scott went to had a snack. Cody gave it Scott. Contrastive prompt: The store Cody and Andrew went to had a snack. Cody gave to Scott.
This approach ensures that by replacing a key entity (here, the indirect object), the resulting label is guaranteed to be incorrect. Similarly, in our translation task, replacing the target language indicator "English" with an irrelevant term such as "Nothing" ensures that the model deviates from the correct translation.
B.3 Token Type Definitions and Examples
IND (Instructional Tokens): Structural or framing tokens that establish the translation context but are not part of the source content. These tokens provide necessary formatting linguistic direction without contributing to the semantic content being translated.
SRC (Source Tokens): The actual input text intended for translation. These tokens represent the semantic content that needs to be converted from the source language to the target language.
TGT (Target Tokens): The translated output tokens in the target language. These represent the model's generated translation of the source content.
Illustrative example: demonstrate this token classification, consider the translation sequence:
English: cloud
+x: z
The token type decomposition for this sequence as follows: This classification scheme enables
Token Type
Tokens
Functional Role
IND
"English"
"# X
Structural framing for translation context Source content for translation
SRC
"cloud"
TGT
Translated output in target language
Table 8: Token type classification for the example sequence
precise analysis of how different token types influence attention mechanisms and translation behavior in neural machine translation models. The IND tokens establish the translation framework, SRC tokens provide the semantic input, and TGT tokens represent the model's generated output, allowing for systematic examination of cross-lingual transfer patterns.
C Task Steering Subspace Probing
Inspired by prior research (Xe al. 2022 Makelov al. 2024), we hypothesize that the space M can be decomposed into two orthogonal subspaces: (i) universal translation
subspace S., embodying translation features common across word translation datasets, and (ii) a specific subspace E., isolating features unique to individual datasets. This decomposition is achieved by optimizing the objective outlined in Equation]] [ following the methodology of Xie et al. (2022); Piratla et al. (2020). We anticipate a lower dimensionality for the universal subspace S, because it represents shared, fundamental patterns; such commonalities can inherently be captured by more parsimonious set of basis vectors, leading to compact representation. Conversely, the specific subspace E, is expected
18

---

to possess higher dimensionality to effectively accommodate the diverse and distinct characteristics particular to each dataset or sample, which necessitate richer representational capacity to capture their unique signals.
The optimal solution Equation 1 efficiently computed via Singular Value Decomposition (SVD), with the detailed procedure outlined in Algorithm|] [ Theorem [ presented in this section, provides the formal basis for this optimal solution. comprehensive proof can be found in Piratla al. (2020):
Xie et al. (2022).
Theorem 1. For any matrix Mc E RdxN Algorithu /
returns matrices Se Rdxx1 E. E RR subject to the constraint Span (Se) 1. Span (Ee).
and r E RR Nxr that minimize Equation |
D More details related to Path Patching
D.1 Standard Path Patching
Output
Output
Comparison
Output
Output
Layer 31
Layer
30
31
30
31
30
31
Hard
Intervention
(mppu embeddingg
Hard
Input embedding
Input embeddinng
Intervention
Gather activations
X
Calculate outputs
Gather activations X
hard interventions
No.
Attention Head
M
MLP Layar
Residual Connection
Input Output
Data flow
Intervention
data flow
Activations given X.
given
Figure 8: Illustration of the method "path patching". It measures the importance of the selected circuit (i.e., the red lines that originate from Head 30 in Layer 0 to Output) for the transformer in completing the task on reference data.
The computation of large language models (LLMs) can be formalized as directed acyelic graph (DAG) (Wang al.) 2023), where nodes represent computational components (e.g., attention heads, MLP layers) and edges denote directional data flow between them. Mechanistic interpretability seeks to reverse-engineer neural networks into interpretable algorithms, leveraging computational circuits as a framework. A computational circuit is a subgraph the model's computational graph M, comprising nodes (c.g., embeddings, attention heads) and edges (e.g., residual connections, projections) that collectively implement specific tasks, such as translation.
The procedure of standard path patching is illustrated in Figure g Activations from all nodes are first recorded. A hard intervention replaces the Sender's activations with those from X_, propagating the effect through paths ' (residual connections and MLPs). Concurrently, other attention heads are frozen to X4 to isolate the Sender's impact. The resulting logits are compared quantify Sender's causal contribution: significant changes indicate critical paths for task execution. Since residual streams and ML.Ps process tokens independently (Flhage et al., 202T), perturbing activations at the END token position suffices to measure effects next-token prediction.
19

---

D.2
Comparison of the proposed subspace-intervened path patching with standard path patching
Standard path patching techniques intervene on the entire activation vector of a component within neural networks (Heimersheim and Nanda [024] Wang et al., (023). However, these activations often exhibit polysemanticity, simultaneously encoding multiple unrelated concepts. This polysemantic nature presents a significant challenge in mechanistic interpretability, as full-vector patching conflates the causal effects of target functions (such as translation) with numerous irrelevant functions encoded within the same vector space.
To address this limitation, our proposed method identifies and intervenes upon low-dimensional sub- spaces specifically responsible for translation within the activation space. This subspace-intervened approach enables the isolation of specific causal mechanisms of translation from confounding functionalities, providing more fine-grained and accurate understanding of the model's internal translation processes. By moving from full-vector to subspace intervention, we achieve targeted and necessary design that facilitates precise mechanistic analysis in large language models.
To validate the effectiveness of our subspace-intervened path patching approach, we conducted comprehensive experiments comparing it with standard path patching baselines across multiple translation directions. Our evaluation encompassed both high-resource (English-Chinesse and low- resource (English-Swahili) language pairs assess the generalizability of method.
We implemented both approaches on the same pre-trained multilingual language model architecture. For standard path patching. we followed the methodology described in prior work, intervening complete activation vectors. For our subspace-intervee intervenne approach, we first identified translation- specific subspaces through targeted projection techniques before performing interventions.
Evaluation metrics included:
Average logit changes when intervening on identified components
Accuracy drop when knocking the top-5 most crucial attention heads
Translation performance measured by BLEU, COMET, and BLEURT scores after targeted supervised fine-tuning (SFT) of the top-32 identified heads
Table presents a detailed comparison between standard path patching and our subspace-inten intervened approach across multiple translation directions. The results demonstrate the superior performance of our method in identifying components critical to translation.
Table 9: Comparison of standard path patching versus subspace-intervenie approach across translation directions
Top Crucial Heads
Avg. Logits
Acc. Drop
Targeted SFT
Translation Pairs
Performance BLEU/COMETT/LEUR
Layer, Head
Change
Knockout Top-5
En-+Zh (standard)
(31, (14, 10), (30. 18)
-2.69%
-25%
8/...99.//624
En En (subspace)
(15, (31, 1I), (18, 26)
4.47%
-39%
28.9/80.5/63.1
7h En (standard)
(15, 19), (31, 22), (14, 10)
-1.71% 7.7%%
-22%
18.5/77.9/62..
Zn (subspace)
(31, (31,
-2.49%
-31%
19.8/78.4/63.1
Sn (standard)
(22, (31, (1,
-3.12%
-28%
1.83/51 .5/40.9
Sn (subspace)
(16, 26), (31, (18,
-6.81%
-42%
3.91/55. 1/43.7
Sw-+En (standard)
(14, (31, (31, (15, 11)
-1.43%
-21%
14.5/67, 1/53.2
Sw-En (subspace)
(31, (30, 18), (14, 10)
-2.01%
-26%
15.9/67. 9/54.0
The results reveal several key findings. First, subspace-intervent method identifies components more critical to translation, as evidenced by the larger average logit changes across all translation directions. For instance, in the English-Swahil translation direction, method produces a logit change of -6.81% % compared to -3.12% with standard path patching, indicating the identification of more influential components.
Second, knockout validation further confirms the superiority of our approach. When the top-5 most crucial heads identified by our method are knocked out, we observe significantly larger accuracy drops compared to standard path patching. This demonstrates that our method more accurately identifies components essential to the translation mechanism.
20

---

Third, targeted supervised fine-tuning of only the top-32 heads identified by our subspace- -nttrveeed approach yields superior translation performance across all evaluated directions. This targeted enhancement capability is particularly valuable for resource-effiient model improvement, as it enables precise modifications the most relevant components without extensive full-model fine- tuning.
These empirical results validate that our subspace-intervende path patching method provides more fine-grained and accurate analysis of translation mechanisms in large language models, addressing the challenge of polysemanticity that limits standard approaches.
D.3
Explanation for the heatmaps.
Figure D provides direct comparison of the impact of patching individual attention heads across different translation directions. The color intensity of each square represents the magnitude of the logit change resulting from patching the corresponding attention head, with deeper red indicating a more significant logit decrease. The consistent deep red of the square at position (8,31) across all six subfigures demonstrates its critical negative impact performance in all tested translation directions. To supplement this visual representation, we provide the specific quantitative values for the average logit decrease when patching head (8,31): These quantitative measurements confirm that
Table 10: Average logit decrease when patching attention head (8.31)
Translation Direction
Average Logit Decrease
Zh En
-1.70
Zh + Fr
-280
Zh Ru
-1.20
En + 7h
-1.10
Fr - Zh
-3.20
Ru + Zh
-5.00
patching head (8,31) consistently and substantially degrades model performance across all translation directions, with the most significant impact observed in the Ru - Zh direction (-5.00 logit decrease).
E
Additional Mechanistic Analysis
E.1
Extend Subspace path-patching to Low-Resource and Typologically Diverse Language Pairs
To validate the universality and robustness of our findings across diverse linguistic scenarios, we extended our analysis to include low-resource and typologically diverse language pairs. Specifically, we incorporated Swahili (sw) and Bengali (bn) as low-resource languages, along with Arabic (ar) as families. All experiments used
typologically distinct language from the Germanic and identical model architectures, training procedures, and evaluation metrics as described in the main paper ensure methodological consistency.
The results presented in Table IT demonstrate that the key findings regarding sparsity and transfer- ability of crucial attention heads persist across these challenging language settings. The proportion of crucial heads remains consistently low 22.05%-3.719% comparable to the high-resource language pairs analyzed in the main paper. This confirms the sparsity phenomenon is not artifact of resource abundance.
Notably, several heads exhibit cross-lingual transferability across diverse language families. Head (8.31) appears as crucial all six language pairs, while heads (18,30) and (10,14) are critical in four pairs each. This consistent emergence of specific heads across typologically distinct languages sug- gests they encode universal translation mechanisms rather than language-specific artifacts. The logits change ratios (-6.81% to 9.17%% further confirm that these heads significantly impact translation quality, with more negative values correlating with lower-resource settings where translation quality is inherently more challenging.
21

---

Table 11: Results on low-resource and typologically diverse language pairs. Heads appearing in at least two language pairs are marked in bold.
Language Pair
Crucial Heads Proportion
Top Crucial Heads (Layer, Head)
Average Logits Change Ratio
En-Sw 3.32%
2.93%
(16,26),31,8) (1,
17)........
-681%
Zh-Sw
-7.19%
En Bn
3.71%
(1,,), 20........
-9.17%
Zh-Bn
2.34%
-8.20%
En-Ar
2.83%
-8.20%
Zh-Ar
2.05%
-8.94%
0-
O-
10
10
5-
810
5-
E10
5-
10
10
10
15-
g15-
15
g15-
15
g15-
00
20-
k20
2
-25
g25
825
30%
30
-10
30
25 30
10 15
30
10
15
Hoads
MIPs
Atteation Hoads
Attoution
(a) Zh + Ar
(b) Zh + Bn
(c) Zh + Sw
500
10
Haardg lopts
bange in lopts
10-
0
0
$20
25.
2
25
10
g225
-50
25
2225
10.
30-
30
-40
25 30 Attention Heads
10
50
10 15 20
MLPss
Altention Hoads
MLPS
Atteation Hoads
MLP
(d) Ar
(e) Bn
Sw
Figure 9; Importance of heads related to translation across different directions. Each square at
position (z, y) refers to the x-th head in the y-th layer. Red (Brown) squares denote heads (MLPs) that have positive impact predicting the target token, while grey (purple) squares indicate heads (MLPs) with a negative effect.
These results substantiate that the sparsity and transferability of crucial attention heads represent fundamental properties of multilingual translation models, independent of resource availability or linguistic typology. The findings reinforce the generalizability of our core conclusions and provide empirical evidence for the existence of universal attention mechanisms in neural machine translation architectures.
E.2 Extend Subspace path-patching to Sentence-level translation
To extend our mechanistic analysis beyond word-level translation, we conducted experiments on sentence-level translation using the WMT23 English-to-CH Soglish-to-Chinese dataset. The experimental pro- cedures followed the methodology outlined in Section of of the main paper, maintaining identical model architectures, training configurations, and evaluation protocols. This extension allowed us to investigate the generalizability of our findings to more complex translation scenarios involving long-range dependencies, contextual variations, and multi-token mappings.
Table 12: Comparison of Crucial Attention Heads and Performance Impact Across Translation Tasks
Performance Metric Change
Performance Drop (Knockout Top-S
Performance Drop out Top-5 Sentence- Levell Heads)
Top Crucial Heads (Layer,
(lower logits or higher means poorer translation quality)
Overlapping Heads)
wond-level
21), (31, II), 26), (16, 26), (31. 8), (26, 30), (20, 20). (14, 16)... 26), (14, (20, 20).
4.47% (logits)
39%
.2%
sentence-level
(14, 16), (14, 13), (22, 26), (28, 18)...
+10.5% (PPL)
-36%
-43%
The causal analysis revealed 46.9% overlap (30 out of 64) between the top crucial attention heads for word-level and sentence-level translation tasks, with representative overlapping heads including
22

---

(18, 26), (20, 20), and (14, 16). This substantial overlap indicates a shared core translation circuit that operates across different levels of translation complexity.
Ablation experiments demonstrated that knocking out five shared heads resulted in significant performance degradation for both word-level (-39% in logits) and sentence-level (-36% in PPL. translation tasks. Conversely, ablating five heads crucial exclusively for sentence-level translation had minimal impact on word-level performance (-2%) but caused substantial degradation sentence-level translation (-43% in PPL). This differential effect highlights the functional specialization of attention mechanisms.
Behavioral pattern analysis further revealed distinct functional roles:
Overlapping heads primarily focused on local syntax and translation indicators, handling fundamental cross-lingus manss-lingual mappings that remain consistent across word and sentence con- texts.
Non-overlapping heads specialized in processing long-range dependencies and broader source contexts, addressing the increased complexity of sentence-level translation where contextual relationships span multiple tokens.
These findings demonstrate that while translation mechanisms are preserved across task complex- ities, sentence-level translation recruits additional specialized attention heads to manage contextual and structural complexities not present in word-level translation. The results validate the methodolog- ical approach of initially isolating word-level mechanisms while establishing the scalability of our analysis framework to more complex translation scenarios.
E.3
Extend Subspace path patching to Multilingual Mathematical Reasoning
To further validate the task-agnostic nature of our method, we applied our analysis framework to multilingual mathematical reasoning using the MGSM dataset (Shi al. 2023 This experiment
demonstrates how our approach adapts to new domains by constructing task specifccc counterfactual
datasets.
Following the methodology outlined in Section 3 we generated counterfactual examples by altering task instructions while preserving the mathematical content. The analysis was performed multilingual transformer model, where we systematically evaluated attention heads across all layers.
We illustrate the counterfactual generation process with the following representative example from our multilingual analysis:
Table 13: Fxample of counterfactual pair generation for multilingual mathematical reasoning. The core problem remains identical while the task instruction changes from numerical answer generation to sentence rephrasing.
Factual Example (X,)
Counterfactual Example (Xef)
X
NETRAAA
ッイ :
!ズ ::
(Shawn has five toys. For Christmas, he got two
(Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now? Rephrase the sentence:)
toys each from his mom and dad. How many toys does he have now? Give the number:)
Our analysis identified sparse set of critical attention heads for mathematical reasoning, comprising only 3.95% of all heads the model. This sparsity pattern aligns with observations from our translation experiments, indicating consistent underlying mechanisms across tasks.
The most influential heads and their impact were quantified as follows:
Top-5 critical heads: (11, 8). (12, 22), (6, 22), (18, 12), (4, 31)
Average logit decrease: 9.76% when ablating these heads
Performance impact: Ablating the top- heads caused 60% drop in task accuracy
23

---

These results confirm that our method effectively identifies components critical to mathematical reasoning across languages. The significant performance degradation upon ablation of these heads validates their functional importance, while the consistent sparsity pattern across tasks demonstrates the robust adaptability of our approach new domains.
The experiment establishes two key properties of our framework: (1) its ability to generalize to multilingual contexts without task-specific modifications, and (2) its capacity to pinpoint functionally critical components even in complex reasoning tasks. The identified heads likely correspond to mechanisms for numerical processing and instruction comprehension, suggesting potential cross-tast similarities in how transformers handle structured reasoning problems.
F
Discussion of the Emergence of Translation-Crucia Components
To provide rigorous quantitative support for these observations, we analyzed the logit changes induced by Supervised Fine-Tuning (SFT) and Continued Pre-training (CPT) relative to the base model. We performed Two-Sample Kolmogorov-Sminn
(K-S) test on the overall logit change distributions and quantified the magnitude of change within the top 32 attention heads, summarized in Table [
The results demonstrate that CPT induces a statistically significant distributional shift (p 0.00001),
while SFT does not (p = 0.355).
To further validate the emergence and refinement of translation-crucial components, conducted comparative causal analysis across three model configurations: (1) a randomly initialized baseline, (2) the multilingual pre-trained LLaMA-2 model, and (3) the SFT-fine-tuned variant. We employed logit change matrix analysis to quantify structural patterns in translation-related attention heads, with statistical significance assessed using distributional shift metrics a significance threshold of p < 0.05.
The randomly initialized model exhibited unstructured logit change matrix with discernible specialized translation heads, indicating the absence of innate translation capabilities. In contrast, the pre-trained LLaMA-2 model developed sparse set of critical translation heads, demonstrating statistically significant distributional shift from the random baseline. This confirms that functional translation circuits emerge during multilingual pre-training.
Subsequent analysis of the SFT-fine-tuned model revealed only a minor distributional shift relative to the pre-trained model, which did not reach statistical significance. This negligible change indicates that supervised fine-tuning primarily enhances or slightly refines pre-existing translation components rather than inducing structural formations.
These results collectively provide empirical support for two-stage development process of translation capabilities: (I) component formation occurs during multilingual pre-training through exposure to diverse linguistic patterns, and (2) component refinement occurs during SFT through targeted optimization. The statistically significant emergence in pre-training versus the insignificant shift during fine-tuning underscores that SFT functions as calibration mechanism for pre-eesaallssed structures rather than an architectural catalyst.
0-
D-
0.
5-
50
20
5.
20
pargee in lighs
10.
10
10
15
25
15-
10
10.
15.
15-
20
0
15
15.
25
20.
25.
50
30
35
-20
-20
30 Huads
1015 20 25
so Heads
-5.0
25 30
MI.S
(a) LLaMA2-7B
(b) 1.BMM2-11BB
(c) Mistral-7B
Figure 10: Comparison of the results of path patching experiments LLaMA2-7B, LLaMA2-13B,
and Mistral-7B (Jiang et al.] 2023) across Zh = En translation task. Each square at position (rr y) refers the zth-head the y-th layer. Red (Brown) squares denote heads (mlps) that have a positive impact on predicting the target token. while grey (purple) squares indicate heads (mlps) with negative effect. For each head/MLP, darker color indicates larger logit difference from the original model before patching.
24

---

10
5
10
10
10
5-
10
5
2.5
10-
50
Charrggg in logits
in
15.
15-
15
15-
15.
00
15-
00
20
20
20
-25
-2.5
-50
30%
-50
30
-50
55.0 30
25 30
10 15
30
10
15
20
MIPs
Atteation Heads
(a) En + De
(b)
En
Fr
(c) En
Ru
20
5-
chenggge logits
5
Simmag logits
change in logits
10
10
10-
10-
15-
15
15
15
20
20
20
25.
25
10
25.
30
-20
30
5. 10
20 25 30
10 15 20 25 30
5 10 15 20 30
Attention Heads
MLPs
Attention Hleads
MLPS
Attention Lieads
MLPS
(d) De En
(c) Fr En
(l) Ru En
Figure 11: Importance of heads related to translation across different directions. Each square at position (xr y) refers the s-th head in the y-th layer. Red (Brown) squares denote heads (MIPs) that have positive impact on predicting the target token, while grey (purple) squares indicate heads (MLPs) with negative effect.
G Additional Detection Results of More LLMs
Crucial Component Detection. Figure extends key component identification to LLaMA2-13B and Mistral-7B. All three models exhibit sparse localization translation-critical attention heads (e.g., 17.24, 16.0) in middle layers, despite architectural differences (e.g., LLaMA2-13B's 40 layers with 40 heads per layer).
Figure 11 illustrates the detection results for bidirectional translation directions (En + X and X + En). While the multi-token nature English tokens results in fewer prominent detection instances, the findings remain consistent with the earlier analysis in Section 44.2 Together, these observations support the conclusion that translation mechanisms utilize a sparse subset attention heads, which aunguage-agnostic thereby underscoring their generalization capacity.
H Additional Experiments for Validating Crucial Components
Further elaboration on selection of correct translation samples for analysis. Focusing correctly translated samples was intentional to eliminate task ambiguity and ensure focused exploration of the translation mechanism.
Comesarru Accurayy (9)
80
12
Incorrect translations could reflect task failure or unrelated issues, complicating the analysis. By selecting the cor- rect translations, we can more accurately trace the role of
80
40
attention heads via path patching. Therefore, our exper- imental setup is appropriate for exploring the translation mechanism LLMs. Using controlled, correct transla- tion dataset aligns with prior interpretation research (c.g., Wang et al. (2023); Zhang al. (2025)), where analyses were conducted on manually curated correct task datasets. This ensures observed patterns directly reflect the trans- lation process rather than error-driven noise. The correct translation selection choice will not bias results for two reasons: The samples used for path patching and those used in subsequent validation are entirely separate. This separation prevents any potential bias introduced by selecting correctly translated samples for path patching from affecting the validity of our conclusive claims.
Kiy Heads
Random Hoads
Key MLP
Rondom MLPs
Companen Knocknuts
10
Figure 12: Translation accuracy changes when components are progressively knocked out.
25

---

English
Engiish sh
Anco when Results
QUENES
ric
Adtuncnnn Nosut
Quech ts
-0.0
Wosrtton fasida
QUIRIES
Fren
Rtn
Dousch
Deusash
Ena/3h-
man
Endko
(a) Source Head (18, 17) (b) Positional Head (1, (1)
(a) Source Head (18, 17) (b) Positional Head (4, 31)
English
92
English
-0.
03
Actorton Reuls
cutnes
-0.0
Fran Gob5
Devisch
96
Englas
FXD
DNU
(c) Position Head (27, 14) (d) Indicator Head (4, 14) (c) Indicator Head (27,14) (d) Indicator Head (4, 14)
Figure 13: The attention values visualization Figure 14: The attention values visualization of the role-classified key heads in En Fr, which the role-classified key heads in En De, which show different characteristics of different crucial show different characteristies of different crucial heads.
heads.
Experimental results on random datasets. We have also replicated the experiment using randomly selected samples (132 samples), including those with translation errors. The results shown in Figure remmmii consistent with original findings, reinforcing the correctness and robustness of our clamms.
I Statistical Significance of Behavioral Patterns Analysis
For our behavioral patterns analysis, we utilized 100 randomly selected Chinese-to- Engllssh (Zh+>En) translation samples. This approach aligns with established practices influential interpretability studies VVoita al. 2019 Wang et al., 2023), which prioritize representative examples over large sample sizes through careful manual inspection to uncover underlying mechanistic behaviors.
Quantitative Analysis of Attention Patterns To validate the statistical significance of our ob- served behavioral patterns, we conducted rigorous quantitative analysis of the key attention pat- tern-specifically, the phenomenon of attention heads focusing on source tokens.
Within sample of 100 translations, this pattern occurred in 81 instances, representing 81 % consistency rate. To establish the statistical significance of this observation, we computed the 95% Wilson score confidence interval, which yielded [72.0%, 87. 37.9%]. This interval substantially exceeds the chance level of 50%, indicating systematic behavior rather than random occurrence.
Furthermore, performed binomial test to evaluate the null hypothesis that the observed pattern occurs at chance level. The test results allowed us to reject the null hypothesis (p < 0.001), confirming the statistical significance of the identified attention behavior across our sample.
Implications for Interpretability These quantitative results reinforce the validity of our qualita- tive analysis approach. The high consistency rate and statistical significance demonstrate that the behavioral patterns we identified are robust and reflect systematic processing mechanisms rather than isolated incidents. This methodological approach, combining targeted quantitative validation with in-depth qualitative inspection, provides a comprehensive framework for interpreting model behaviors in neural machine translation systems.
J Additional Behavioral Analysis of Translation Directions and More LLMs
Extended Behavioral Pattern Analysis Crucial Heads Across Diverse Language Pairs. To investigate the consistency of crucial head types and broaden the scope of our behavioral pattern
26

---

Ateetto Roodls
QUaamS
Allettn Kow
QUEREES
Doza
(a) Source Head (18, 17) (b) Positional Head (4, 31)
(a) Source Head (18, 17) (b) Positional Head (4,31)
22
Rovls
VITEEEE
Danck
Deasa
-LO
(c) Indicator Head (27, 14) (d) Source Head (4, 14)
Indicator Head (27, 14) (d) Indicator Head (4, 14)
Figure 15: The attention values visualization of Figure 16: The attention values visualization of the role-classified key heads in Zh + Fr, which the role-classified key heads in Zh = De, which show different characteristics of different crucial show different characteristics of different crucial heads.
heads.
analysis, we conducted evaluations on multiple translation directions beyond the initial English- to-Chinese (Zh=En) focus (see Figure 4 This extended analysis incorporated English=France (En=Frr,, English+ (En+De), Chinese + France (Zh=Fr), and Chinese = German
(Zh=De) language pairs, as illustrated in Figure 13
and 16 respectively.
Our findings indicate that while the fundamental insights observed in En largely hold across other language pairs, nuanced variations in crucial head behavior did emerge. Specifically, several source heads (e.g., (18, 17)), and position heads (e.g., (4, 31)) exhibited consistent cruciality and behavioral patterns across all the En, En->Fr, En=>ee, Zr,>Fr, and Zh De translation directions. This suggests degree of universality for certain attention mechanisms irrespective of the specific language pair.
However, the cruciality of some heads demonstrated language-pin -air dependency. For instance, heads (4, 14), (27, 14), identified as indicator heads, were also crucial for the En=*Fr and Zh= directions but did not exhibit the same type of functional role. Such variations indicate that while the identified categories crucial heads (source, position, indicator) are generally stable, the specific instantiation and relative importance of individual heads within these categories be influenced by the linguistic characteristics of the language pair in question. Despite these specific variations, the core observation of distinct functional roles for different head types remains robust. This comprehensive analysis across multiple language pairs has been incorporated to underscore the generalizability. as well as the language-specifer nuances, of the identified behavioral patterns.
0.25
0.25
0.25
(MLP (SRC))
(MLP In (SRC)
(MLP (SRC))
0.20
(MLP (RAND)) )1
Cosine similarity
0.20
(MLPim. (RAND) )
Cosine Similarity
0.20
(MLPh,.. (RAND))
(MLPim. (IND))
(MLPim. (INDH
(MLPI.. (IND)
0.10
0.10
0.10
0.05
0.05
0.05
0.00
1.0
15
10
15
20
25
Layer
Layer
Layer
(a) LLaMA2-7B
(b) LLaMA2-1B 13B
(c) Mistral-7B
Figure 17: We investigate the projection of each MLP layer input (MLP..) along the direction of the source language, indicator, and random English tokens (IND), and (RAND]), respectively.
27

---

0.5
0.25
MLP (TGT))
(TGT))
IMLPowt MLPI. (TGT))
(MLP MLP (RAND);
Cosine Similerty
(MLP MLP (RAND))
Cosine Similarity
(MLP MLP (RAND))
0.15
0.10
0.10
0.10
0.05
0.05
0.05
0.00
Layer
Layer
Layer
(a) LLaMA2-7B
(b) LLaMA2-13B
(c) Mistral-7B
Figure 18: We investigate the projection of each MLP layer (M mL) along the direction of the target language, and random English tokens ((TGT) (i.e., right translation), and (RAND) (i.e.. wrong translation)), respectively.
Analysis of Crucial MLPs. Figures 17 and reveal consistent MLP dynamics across models. For MLP (opput(SRR)..UU/ points at layers (13-18-28) for LLaMA2-7B, (13-18-35) for I.I.MA2-338 and (13-20-28) for Mistral- 7B. Similarly, MI.Pout MT.P,, and target token (TGT) similarities show stabilization-o-inrrease patterns with identical inflection layers. This synchronization across models indicates a shared computation mechanism: attention heads initiate translation processing, which MLPs subsequently refine. These results demonstrate robustness across architectures and scales.
similarities, trends follow beeending-decenddingg phases with inflection
[MLPPar MiL EMI
MMLP MLP EN
DML Pan MLPs. ENI
0.35
[MLPP
MLPx
MMPPa NLPs. IT..
Similanit
0.35
Similanty
0.35
0.25
0.30
0.20
0.20
0.15
0.15
0.15
0.05
0.00
0.00
0.05
-0.10
(a) LLaMA2-7B
(b) LLaMA2-13B
(c) Mistral-7B
Figure 19: We investigate the projection of each MLP layer (MLPou. - MLP... along the direction of the different languages.
Cross-Lingual Bridge Translation. We extend our analysis to non-English pairs (e.g.. French/Japanese Chinese) by examining token-level dynamics. As shown in Figure 19 similar- ity trends between M - M LPn. representations and cross-lingual embeddings align with tridge-translation hypothesis: in layers 15-24, English-centric latent representations dominate across LLaMA2-133 13B and Mistral-7B, with similarity declining sharply in layers 25-32. This reinforces the observed paradigm where LLMs internally map source languages to English- like representations be- fore generating target outputs, corroborating findings in multilingual latent alignment studies (Wendler al. 2024 Zhao al. 2024b). The consistency across both architectures underscores the generality of English's s intermediary role.
K Experimental Setup Details
Following the gradient rescaling method proposed by (Yu al.) 2025). gradients are adjusted by factor of H. where H is the total number of attention heads in layer and h represents the updated heads in the same layer. For model fine-tuning, we use Llama2-7B and Llama2-13BB with learning rate of 2 x 10-5 batch size of 128, and train for epochs. The warm-up ratio is set to 0.02, and weight decay is configured 1. All experiments are conducted on cluster of NVIDIA A100 80 GB GPUs.
28

---

Translation Tasks
Generic Tasks
Commonsense Reasoning
Train Speed
Tuned Params,
En->Zh
En=DDe
En
MMLU
Models
BLEB//CCMMET//BBURTT
Acc.
Acc.
LLaMA2-7B
13.0/64.2/49 17.0/74.1/55.9
12.8/70.5/2..4
45.9
55.3
+ Full SFT
17sam./scc.
6.7B
30.3/80.7/62.9
27.9/78.3/63.7
19.5/80.0/63.22
40.2
50.0
Targeted SFT
/se.
0.27B
30.7/81.4/643
4//44.3
27 27.6/88.4/633.8
20. 1/80.4/63.6
56.0
Random SFT
3ssam.
0.27B
26.4/79.3/61.6
22.7/76.2/60.3
46.1
55.2
1.MMA2-13B
23.0/77.50
17.1667.7/52.8
15.6/72.9/55.
55.1
58.4
Full SFT
/sea
13.0B
32.8/81 .8/64.4
29.8/80.066 0/65.8 8
20.7/81 6/65.0
53.7
56.4
+ Targeted SFT
28eam//sec.
0.32B
33,4/82.2/66 2/64.8 8
30. 1/80.1/655 1/65,9
21,3/81
8/65.3
54.9
58.1
Random SFT
28sam//sec.
0.32B
28.8/80.6/63.7
24.6/78.3/62.9
17.3/80.0/62.88
55.0
58.2
Mistral-7B
13,7/68 0/49.6
15.6/
11.2/65. 1/48.1
62.7
59.2
+ Full SFT
/ssam/
6.7B
31.1/80.6/00 6/63.4
26.5/77
4/62.8
19.6/79.5/6255
43.0
40.8
+ Targeted SFT
33sam./sec.
0.27B
31.9/8.0066 0268.0665.1
26.3/78.0/63./
20.5/79.9/63.
62.5
Random SFT
33sam./sce. 0.27B
27.5/79.5/61.6
22.2/75.5/59.8
15.6/77.4/0055
62.4
59.2
Table 14: The evaluation results of En- translation (average WMT23 and WMT24 evaluation
results) and generic tasks of different SFT strategies.
Translation Tasks
Generic Tasks
Commonsense Reasoning
Train Speed
Tuned Params.
Zh >En
De De
Ru
MMLU
Models
#LEU>//OMETY/BLBUU
Acc.
Acc.
LLaMA2-7B
15.6/73.1/56
24.8/76.866.J
20.2/73.8/66 8/60.3
45.9
55.3
+ Full SFT
17sam//scc.
6.7B
20.4/78.7/63.9
35.4/83.4770.7
25.8/79.8/67.6 6
42.6
50.2
+ Targeted SFT
33cam./scc.
0.27B
21.7/79. 1/6444
37.1/83.7711.4
27.8/80.3/68.4
46.0
55.7
Random SFT
3esam./se.
0.27B
16.9/76.9/61.1
32.5/81.6/68.1
23.7/78.2/65.3
45.9
LLaMA2-13B
17.3/74.0/57.8
27.0/78.0/63.
22.2/74.9/61.5
55.1
58.4
Full SFT
/scem./sce.
13.0B
22.4/79.5/65. 5/65.3
36.9/84.0771.6
27. 27.8//
55.3
Targeted SFT
28sam./sec.
0.32B
23.6/80.5/66.5
78/7274
29.7/81 .5/69.3
54.9
58.1
Random SFT
28sam./sec.
0.32B
19.0/78.
14.2/81.8/68..
25.3/79.3/66.
55.5
58.8
Mistral-7B
16.9/74.3/58.1
26.6/77.9/63.9
22.6/75.3/62.5
62.7
59.2
+ Full SFT
/seam./sec.
6.7B
19.7/78.4/63.
32.0/82. 2/69.0
24.0778.7/666
40.3
50.3
+ Targeted SFT
3seam
0.27B
21 21,2/79.2/66 2/64.3
33.7/83.0/70.
26. 6/799/666666
62.9
59.1
Random SFT
33sam./sec. 0.27B
16.1
29.3/80.6/66.8
21.4/7.1163.9
62.5
Table 15: The evaluation results of X=>En translation (average WMT23 and WMT24 evaluation results) and generic tasks of different SFT strategies.
L Comparison Experimental Results on More LLMs
We investigate whether our method generalizes to larger LLMs (Llama-2-13BB and diverse architec- tures (Mistral-7B) As shown in Tables and [5] Targeted SFT exhibits three consistent advantages across LLMs: (I) Enhanced translation performance, particularly in X En, surpassing Full SFT and significantly outperforming Random SFT: (2) Generalization preservation, maintaining baseline non-translation task performance unlike Full SFT: (3) Training efficiency, modifying fewer than 5% of parameters and reducing training time by 50% compared to Full SFT.
M
Additional Analyses of English-centrie representation
M.1
Correlation Analysis between English Similarity and Translation Quality
To quantitatively establish the relationship between English-centric representations and translation performance, we conducted a comprehensive correlation analysis. We measured the Pearson correla- tion coefficient between the cosine similarity of intermediate representations English embeddings and three translation quality metrics: BLEU-1, chrF, and TER scores. This analysis was performed across 12 typologically diverse non-Engllish non-English language pairs at varying resource levels.
All correlation analyses were conducted using a standardized evaluation framework. We selected 12 language pairs spanning diverse language families and resource levels, including both high-
29

---

resource (e.g., German-Frence and low-resource (e.g., Swahili- Hauss)) combinations, Translation quality was measured using BLEU-1, chrF, and TER metries computed against professional human reference translations. Cosine similarity was calculated between intermediate representations and target language embeddings using the model's native embedding space.
For the pivot language investigation, we used identical architectures and evaluation protocols for both Llama and Qwen2.5 models to ensure comparability. The logits lens analysis was performed by extracting hidden representations at each layer and projecting them into the model's vocabulary space using the unembedding matrix. Layer-wiss wise similarity was computed against embeddings of pivot language tokens.
The results, summarized in Table T6 demonstrate strong and statistically significant correlation between English similarity and translation quality. The average correlation coefficients across all language pairs were 0.905 for BLEU-1, 0.873 for chrF, and -0.919 for TER. These findings provide empirical evidence that the English-centricity phenomenon is not merely superficial but fundamentally influences translation outcomes.
Table 16: Pearson correlation between English similarity and translation quality metrics
Correlation with English Similarity
BLEU-1 Score
chrF Score
TER Score
Average across 12 language pairs
0.905
0.873
-0.919
M.2 Investigation of Pivot Language Determinants
We hypothesized that the emergence of English as a pivot language stems from its dominance in the pre-training corpus. To test this, we analyzed two models with contrasting pre-training distributions: the Llama models, pre-trained on corpus with overwhelming English dominance (Touvron et al. 2023. and Qwen2.5, pre-trained on a corpus with predominant Chinese data (Yang al. 2024).
Our experiments revealed a clear correspondence between corpus dominance and pivot language emergence. In the Llama models, English consistently emerged as the pivot language. Conversely, in Qwen2.5, Chinese emerged as the pivot language instead of English. This cross-model comparison provides preliminary support for our hypothesis that the pivot language is determined by the dominant language in the pre-training corpus.
M.3 Qualitative Analysis via Logits Lens
To elucidate the internal mechanism through which the pivot language emerges, we performed logits lens analysis following established methodologies (Zhao et al.] 2024b] Wendler et al. (2024). This technique allows visualization of how representations evolve through the model's 's layers during translation.
The analysis reveals consistent pattern: source language representations progressively shift toward their pivot language counterparts intermediate layers before transitioning to the target language. For example, when translating "#" (Chinese) to "voiture" (French). the representation explicitly resolves to the English word "car" in layers 19-27 before shifting to "voiture" in the final layers. This demonstrates mechanistic pathway where the pivot language serves an intermediate representation bridge during translation.
M.4 Exploration of English Latent Representation Regarding Gender and Formality
Our investigation extends to analyzing how the English pivot handles linguistic features without direct English equivalents, specifically grammatical gender and formality. This analysis provides crucial insights into the limitations and capabilities of the pavot-based multilingual translation approach.
We conducted targeted analysis using datasets created for French (fr) and Spanish (es), focusing on two key linguistic dimensions: gendered professions and formal versus informal expressions.
For gendered professions, we utilized the FBKK-MT/gender-bas-- PE dataset (Savoldi et al. 2024). For formal versus informal expressions, we curated a list of common formal and informal ex expressions in both languages. Sample instances from these datasets are presented in Tables 17 and 18
30

---

Table 17: Examples of gendered professions in French and Spanish
Profession (English)
French (Masculine)
French (Feminine)
Spanish (Masculine)
Spanish (Feminine)
Actor
Acteur
Actrice
Actor
Actriz
Waiter
Serveur
Serveuse
Camarero
Camarera
Baker
Boulanger
Boulangère
Panadero
Panadera
Nurse
Infirmier
Infirmière
Enfermero
Enfermera
Table 18: Examples of formal versus informal expressions in French and Spanish
Category
French (Informal)
French (Formal)
Spanish (Informal)
Spanish (Formal)
People (man)
mec
un homme
un tio
hombre
Car
une bagnole
une voiture
un coche
un automóvil
Work Job
boulot
travail l'argent
curro
trabajo
Money
le fric
pasta
el dinero
Applying the analysis methodology from Section 5, we measured both the intermediate representa- tion's 's similarity to the English pivot and the final translation accuracy. Our findings reveal a critical asymmetry in how these features are processed, as summarized in Table 19
Table 19: Analysis of gender and formality features in English latent representation
Language Feature
Avg. Cosine Similarity to English Representation Translation Accuracy
Gender (Male Professions)
0.32
73%
Gender (Female Professions)
0.11
48% %
Formality (Formal Expressions)
0.31
65% %
Formality (Informal Expressions)
0.34
69%
The results demonstrate that male- gendeeeedeeed professional nouns are processed effectively, with their representations showing high similarity to the English pivot (0.32) and resulting in high translation accuracy (73%). contrast, the representations for gennee-eendered nouns show significantly lower similarity (0.11), which correlates with a dramatic drop in accuracy to 48%. Interestingly, both formal and informal expressions are processed with comparable accuracy, suggesting the model preserves this feature through the intermediate representation.
We hypothesize that this gender-specific failure is due well-documented biases in large-scale training corpora, where cemale-gendered terms are less frequent (Ding et al. (025). The model's reliance on biased English latent space makes unable robustly encode and transmit grammatical gender information that is explicitly marked in the source language but often neutralized in English.
N
Supplementary Experiments
N.1 More evaluation results of targeted SFT on domain-adaptive translation
To further evaluate the broader applicability of our approach beyond general-domain translation, we conducted additional experiments specialized domains. Specifically. we tested our method on medical and legal translation tasks using established benchmarks: ELRC-Medical V2 for English-to- German medical translation and M3T for English-to-Chinese legal translation. For these specialized domain experiments, we maintained the same training configurations as described in the main paper. We compared three approaches:
Full SFT: Supervised fine-tuning of all model parameters
Targeted SFT: Our proposed approach of fine-tuning only specific attention heads
Random SFT: Fine-tuning of randomly selected parameters (baseline)
Evaluation was performed using standard metrics, including BLEU, COMET, and BLEURT scores, to provide a comprehensive assessment of translation quality.
31

---

The performance of each approach on specialized domain translation tasks is presented in Table 20 The results demonstrate that our Targeted SFT approach remains highly competitive in specialized
Table 20: Performance comparison on specialized domain translation tasks.
Full SFT
Targeted SFT
Random SFT
Lang Pair
Domain
BLRU/COMET/BLURT BLEU//CMET/BLLLN BLEU/COMET/BLERRY
En + De
Medical Legal
$100/88.579..
39.9/87 4/77.5 45.8/89.2/78..
08.99/3.9/733. 8.07/75.2/66
En + Zh
52.2/90.5/80.5
domains, significantly outperforming the Random SFT baseline across all metrics. However, it does not match the performance of Full SFT in these specialized domains. We attribute this performance gap to several factors:
1. Dataset Distribution and Overfitting: Since the training and test sets in specialized domains typically share the same distribution (via a split of one datasct), Full SFT is more prone overfitting the specific characteristics of the domain. In contrast, our Targeted SFT approach maintains better generalization by limiting parameter updates.
2. Domain-Specific Patterns: Specialized domains such as medical and legal texts exhibit unique syntactic structures and low- uwwfrreuenccy terminology that may require modifying more parameters than our targeted approach adjusts. These domain-spectie patterns might be distributed across a broader set of model components.
3. Head Specialization: Attention heads optimized for general-domait translation may not fully overlap with those essential for specialized domains. Different linguistic phenomena in specialized texts might activate different attention mechanisms that are not targeted by our approach.
These findings reveal important trade-off between parameter efficiency and peak performance in specialized domains. While our Targeted SFT approach offers significant computational advantages and maintains competitive performance, achieving state-of-the- results highly specialized domains may require more extensive parameter modification. This represents an interesting direction for future investigation, as discussed in Section 6 of the main paper.
N.2 Supplementary Analysis of Potential Cultural Bias Amplification by Targeted SFT
Machine translation systems face the challenge of linguistic hegemony, where dominant languages may impose their cultural frameworks and expressions onto less dominant languages. This phe- nomenon can result in the loss of cultural specificity and nuance in translations. To evaluate whether targeted fine-tuning of only the crucial heads responsible for translation mechanim might inadver- tently amplify such translation biases, we conducted a dedicated analysis focusing on the preservation of culturally specific terms.
We assessed translation quality using the CAMT dataset (Yao al. 2024). which contains culturally specific terms and expressions across multiple domains. For evaluation, we employed the CSI-Match metric (Yao et al., 2024), specifically designed to measure the translation accuracy of culturally specific items. The CSI-Match metric operates by comparing translations of culture-specific concepts against reference translations produced by native speakers, with scores calculated based on semantic similarity and cultural appropriateness. The metric ranges from 0 to 100, with higher scores indicating better preservation of cultural specificity and thus lower risk of linguistic hegemony (Yao et al. 2024 Conia et al. 2024).
We compared our proposed Targeted SFT approach against three baselines:
1. Base model: Llama-2-7B without fine-tuning
2. Full SFT: Standard full palamerrrettr fine-tuning on the translation dataset
3. Random SFT: Fine-tuning on randomly selected parameter subsets of equivalent size to our targeted approach
For all models, we evaluated English-to-Chinese (En-*ZM translation performance using multiple metrics: BLEU, COMET, BLEURT, and CSI-Match. All experiments were conducted using identical hyperparameters and evaluation protocols to ensure fair comparison.
32

---

The performance of all models across different evaluation metrics is presented in Table 21
Table 21: Translation performance and cultural specificity preservation across different fine-tuning
approaches for Siglish-to-Chiness translation.
Model (En->Z.h
BLEU
COMET
BLEURT
CSI-Match
Base (Llama-2-7B)
19.54
73.57
51.02
16.12
w/ Full SFT
25.50
79.35
58.28
18.44
w/ Targeted SFT
25,85
79.58
58.64
18.62
w/ Random SFT
19.98
74.73
52.88
16.13
The results demonstrate that our Targeted SFT approach achieves a CSI-Match score of 18.62, which comparable to the more resource-intensive Full SFT baseline (18.44). Statistical analysis using a paired t-test revealed no significant difference between these two approaches (p = 0.42). This indicates that our targeted method successfully improves translation performance without introducing additional risks of cultural bias amplification compared to standard full fine-tuning, Notably, both the Base model and Random SFT approach showed significantly lower CSI-Match scores (16.12 and 16.13, respectively), suggesting that neither preserves cultural specificity as effectively as the more systematic fine-tuning approaches. The minimal difference between the Base model and Random SFT indicates that arbitrary parameter updates do not substantially improve cultural specificity preservation. Across all metrics, our Targeted SFT consistently performed least as well as Full SFT, confirming its efficiency and effectiveness in maintaining translation quality while preserving cultural nuances. The marginal improvement in CSI-Match score over Full SFT, while not statistically significant, suggests that our targeted approach may offer slight advantages in preserving cultural specificity. This analysis provides empirical evidence that our targeted fine-tuning approach does not exacerbate linguistic hegemony risks while maintaining competitive translation performance across standard quality metrics.
N.3 Additional Qualitative Analysis of Targeted Supervised Fine-Tuning
Performance-Efticien Trade-ofl's Tables and present a comprehensive quantitative analysis of the trade-offs between translation performance, training efficiency, computational cost, and catas- trophic forgetting targeted supervised fine-tuning (SFT). The results demonstrate that incrementally increasing the number of fine-tuned attention heads yields progressive improvements in translation performance. However, this enhancement comes with proportional increases in memory consumption and training time. Notably, excessive tuning of attention heads exacerbates catastrophic forgetting effects, leading to significant degradation in the model's general capabilities across non-translation tasks.
Error Analysis of Underperforming Cases An in-depth error analysis was conducted on underper- forming Chinese-to- to-Egliih (Zh-En) translation cases to identify systematic failure patterns. The analysis revealed three primary error categories accounting for over 70% of significant performance gaps:
Style, Diction, and Idiomatic Expressions Targeted SFT frequently produces overly literal transla- tions that fail to capture appropriate stylistic and idiomatic expressions. For instance, the phrase "#i Ii & ih
"0 19's impacts the pharmaceutical showcase) was translated as "COVID-19's impacts on the drug market" instead of the contextually appropriate "pharmaceutical showcase." This pattern indicates limitations in capturing domain-specift terminology and idiomatic nuances.
Noisy Data Robustness The approach exhibits reduced resilience to ambiguous noisy input data. representative example is the mistranslation of " (Article 9) as "9th draft" rather than the correct legal terminology "Article 9." This vulnerability suggests challenges in handling ambiguous lexical items and domain-specific abbreviations.
Factual Hallucinations The model occasionally generates unsupported factual details not present in the source text. For example, the input " f T (TKT K....." (The charging indication light on the charging box is not yet fully charged, the red light is on...) was erroncously
33

---

expanded to include "green light" in the translation, introducing information absent from the original text.
Optimal Configuration and Performance Our method achieves a performance ceiling statistically comparable full fine-tuning while significantly reducing computational overhead. By exclusively tuning the 64 attention heads most critical for translation tasks, we maintain translation quality within 1% of full fine-tuning performance while reducing memory requirements by 42% and training time by 38%. This optimal configuration, empirically validated in Tables and S demonstrates that targeted SFT can effectively balance performance gains with computational efficiecc when applied selectively to the most relevant model components.
34

---

NeurIPS Paper Checklist
The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:
You should answer [Yes], [No], or [NA].
[NA] means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.
Please provide short (1-2 sentence) justifiicton right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.
The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation.
While "[Yes] " is generally preferable to "[No]", ", it is perfectly acceptable to answer "[No] provided proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] or "[NA] is not grounds for rejection. While the questions are phrased in binary way, we acknowledge that the true answer often more nuanced, so please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only use the provided macros for your answers.
Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The main claims made in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes]
35

---

Justification:
Limitations are discussed in Appendix A
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model vell-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification:
The proof for theoretical results and theorems are proved in Appendix
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
The reproduction information provided in Section 6.1 and Appendix
K
Guidelines:
The answer NA means that the paper does not include experiments.
36

---

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g.. with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors welcome describe the particular way they provide for reproducibilityy In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: The paper provides the anonymous GitHub link for open access to the data and code.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips. cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (c.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to to reproduce the results. See the NeurIPS code and data submission guidelines (https
//nips.cc/public/eee.c wttp://ddd//ooeeuuiiieeeeeeeeeee ssi ssionPolicy) for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
37

---

Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification: Experimental setting/details are provided in Section 6.1 and Appendix K Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification:
We show error bars in Section 5.1 and Section 6.1
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results accompanied by error bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
OK to report 1-sigma error bars, but one should state The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that out of range (e.g. negative error rates).
If error bars reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Experimental compute resources are provided in Appendix K
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
38

---

The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics htttps://nuuriss. http://uwrr.p.ccopoo/////cccccee
Answer: [Yes]
Justification: Yes, the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations their jurisdiction).
Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
We have provided broader impacts discussions Appendix A
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has socictal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The paper doesn't 't have such risks.
Guidelines:
39

---

The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
Yes, we have followed the proper use and credited the owners for the assets as shown in Section 3 and Appendix B.1
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website). the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re- re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. If this information is available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer:
[Yes]
Justification: The newly constructed analysis dataset is well documented Scction S and Appendix B.I
B.2
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moddl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
40

---

Answer:
[NA]
Justification:
This paper does not involve crowdsoureing.
Guidelines:
The answer NA means that the paper does involve crowdsoureing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurIPS Code of Ethies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Question:
Answer: [NA]
Justification: This paper does not involve crowdsourcing.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing. editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
Justification:
The core method development does not involve LLMs as any important components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non otanndaddrdd components.
Please refer to our LLM policy (https://nnt /ct/oonfeeenccc/2225/////////// for what should or should not be described.
41


Reviews:
[{"review_id": "6TqDTX8Or0", "reviewer": "Reviewer_ByrS", "summary": "The paper reveals the sparsity and English-centric bridging mechanism in LLM translation through subspace intervention and component-level analysis. Based on this discovery, it proposes an efficient targeted fine-tuning method that achieves full-parameter fine-tuning performance by optimizing only 64 attention heads while doubling training speed, laying the foundation for interpretable machine translation research.", "questions": "1. Unsupported percentage claims - the 60% and 70% overlap rates lack clear evidence in Figure 1.\n2. Figure 1 contains six subfigures, each showing position (8,31), but no direct comparison demonstrates the claimed logit decrease when patching this head, and red/brown and gray/purple are difficult to distinguish.\n3. The claimed 70.0% impact in MLP detection results isn't visually supported in Figure 1 - color changes from white to dark only show ~50%+ difference.\n4. Figure 2 lacks a baseline reference line, only showing accuracy differences when removing different key heads and key MLPs.", "limitations": "The potential bias amplification risks associated with fine-tuning critical components (e.g., whether the English-centric bridging mechanism reinforces linguistic hegemony) were not discussed. It is recommended to supplement this analysis.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Quality：​\nThe proposed Subspace-Intervened Path Patching technique precisely identifies translation-critical components by comparing activation differences between positive and negative samples. The experimental design covers multiple language pairs (e.g., Chinese-English, German-French) and model scales (LLaMA2-7B/13B, Mistral-7B), ensuring high credibility of results.\n\nClarity：\nThe subspace decomposition and path patching process are clearly described, enhanced by visualizations for better understanding.\n\nSignificance：​​\nIt systematically uncovers the sparsity mechanism and English-centric bridging pattern in LLM translation for the first time, filling a gap in LLM interpretability research.\n\nOriginality：​\nBy combining causal analysis with low-rank subspace decomposition, it proposes a novel path patching method that surpasses traditional activation patching.\n\nWeakness：\nThe study does not explore the implications of its findings for domain-adaptive translation (e.g., medical, legal).\nIt does not evaluate the long-term stability of models after targeted fine-tuning.", "was_revised": true, "final_justification": "Thank you for the detailed response and additional experiments. I confirm that all my concerns have been fully addressed.", "rebuttal": "Dear Reviewer ByrS,\n\nThank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:\n\n> Weakness #1: Limited evaluation on domain-adaptive translation\n> \n\nWe thank the reviewer for their insightful feedback regarding broader applicability and long-term implications.\n\nOur primary focus is general-domain translation, and we acknowledge the importance of specialized domains. We therefore conducted new experiments using medical (ELRC-Medical-V2[1], En→De) and legal (M3T[2], En→Zh) benchmarks:\n\n| Lang Pair | Domain | Random SFT (BLEU/COMET/BLEURT) | Targeted SFT (BLEU/COMET/BLEURT) | Full SFT (BLEU/COMET/BLEURT) |\n| --- | --- | --- | --- | --- |\n| En → De | Medical | 28.9/83.9/73.8 | 39.9/87.4/77.5 | 41.0/88.5/79.1 |\n| En → Zh | Legal | 8.07/75.2/65.8 | 45.8/89.2/78.1 | 52.2/90.5/80.5 |\n\nAnalysis shows Targeted SFT remains highly competitive—significantly outperforming random baselines—but doesn’t match Full SFT in specialized domains. We attribute this gap to:\n\n1. Since the training and test sets share the same distribution (via a split of one dataset), Full SFT is prone to overfitting, but Targeted SFT is not.\n2. Domain-Specific Patterns: Unique syntax and low-frequency terminology may require modifying more parameters than our targeted approach adjusts.\n3. Head Specialization: Attention heads optimized for general-domain translation may not fully overlap with those essential for specialized domains.\nThis reveals a trade-off between parameter efficiency and peak performance in specialized domains, now discussed in Section 6 as future work.\n\nWe appreciate these constructive comments, which strengthened our work. This point is added to our discussion for future investigation.\n\n> Question #1: Unsupported percentage claims\n> \n\nTo quantitatively address your concerns regarding the percentage claims, we provide detailed statistics in the following paragraphs. The following numeric results show that while some language pairs have moderate overlap (e.g., Chinese-English/English-Chinese at 40.62%), others exhibit significantly higher rates. Specifically, the overlap rates are:\n\n- 40.62% between Chinese-English (`zh-en`) and English-Chinese (`en-zh`).\n- 62.50% between Chinese-French (`zh-fr`) and French-Chinese (`fr-zh`).\n- 75.00% between English-Chinese (`en-zh`) and French-Chinese (`fr-zh`).\n\nThe original statement intended to convey that overlap rates for many language-direction pairs could reach as high as 70%. We acknowledge that this was not articulated with sufficient precision. We have corrected the text to explicitly state the specific pairs corresponding to these higher percentages, thereby removing ambiguity and directly linking our claim to the evidence in Figure 1.\n\n> Question #2: Elaboration on concerns regarding Figure 1\n> \n\nWe thank the reviewer for their careful attention to Figure 1, and we wish to clarify that Figure 1 is designed to provide a direct comparison. The color intensity of each square visually represents the magnitude of the logit change resulting from patching the corresponding attention head—a deeper red indicates a more significant logit decrease.\n\nThe consistent deep red of the square at position (8,31) across all six subfigures demonstrates its critical negative impact on performance in all tested translation directions. To supplement this visual data, we provide the specific quantitative values for the average logit decrease when patching head (8,31):\n\n- Zh → En: -1.70\n- Zh → Fr: -2.80\n- Zh → Ru: -1.20\n- En → Zh: -1.10\n- Fr → Zh: -3.20\n- Ru → Zh: -5.00\n\nThese results confirm that patching the head (8,31) consistently and substantially degrades model performance.\n\nTo clearly distinguish the results from heads and MLPs, we adopted a color scheme consisting of blue, orange, sky blue, vermilion, bluish green, yellow, and reddish purple to ensure all values are easily distinguishable. We appreciate your thorough review which greatly improve the clarity and presentation of our paper.\n\n> Question #3: Unsupported percentage claims of MLP\n> \n\nWe thank the reviewer for their careful observation and for the opportunity to provide clarification.\n\nWhile we agree that a visual inspection of the heatmap might suggest an average impact closer to 50% in some language directions, our claim of reaching 70.0% is based on the maximum effect observed in our data in several language directions, not the average. The heatmap serves as a qualitative illustration of the overall trend, whereas the specific claims in the text are supported by precise quantitative results.\n\nTo make this explicit, the exact impact values for the key language pairs in Figure 1 are:\n\n- French → Chinese (`Fr → Zh`): −70.83%\n- English → Chinese (`En → Zh`): −66.87%\n- Russian → Chinese (`Ru → Zh`): −56.64%\n- Chinese → Russian (`Zh → Ru`): −55.79%\n- Chinese → English (`Zh → En`): −52.66%\n- Chinese → French (`Zh → Fr`): −50.07%\n\nAs the data shows, the impact for the `Fr → Zh` pair is indeed over 70.0%. To prevent any future ambiguity, we have revised the manuscript to explicitly state this peak numerical value when referencing Figure 1, ensuring the claim is directly and unmistakably supported.\n\n> Question #4: Elaboration on concerns regarding Figure 2\n> \n\nWe thank the reviewer for the opportunity to clarify the experimental design shown in Figure 2. The performance degradation from removing *randomly selected* heads and MLPs is the intended baseline for this analysis. The figure's purpose is to demonstrate that the components identified by our method are significantly more impactful than arbitrarily chosen ones. Therefore, this \"random removal\" condition provides the most direct and rigorous baseline for validating our method's precision.\n\nAn obvious note specifying the baseline has been included in the revised manuscript to provide a clearer and comprehensive comparison of the experimental results.\n\n> Limitation #1: Supplemental analysis of potential bias amplification\n> \n\nWe thank the reviewer for this insightful comment. To empirically address the potential risk of linguistic hegemony, we have conducted a dedicated analysis to evaluate whether our targeted fine-tuning approach amplifies translation biases.\n\nWe assessed our method using the CSI-Match metric [3] on the CAMT dataset [3]. CSI-Match is designed to measure the translation accuracy of culturally specific terms, where higher scores indicate a lower risk of linguistic hegemony [3, 4]. We compared our Targeted SFT against the Base model, Full SFT, and Random SFT baselines.\n\n| Model (En→Zh) | BLEU | COMET | BLEURT | CSI-Match |\n| --- | --- | --- | --- | --- |\n| Base (Llama-2-7B) | 19.54 | 73.57 | 51.02 | 16.12 |\n| w/ Full SFT | 25.50 | 79.35 | 58.28 | 18.44 |\n| w/ Targeted SFT | 25.85 | 79.58 | 58.64 | 18.62 |\n| w/ Random SFT | 19.98 | 74.73 | 52.88 | 16.13 |\n\nThe results show that our Targeted SFT achieves a CSI-Match score comparable to the more resource-intensive Full SFT baseline, with no statistically significant difference between them. This finding provides strong evidence that our targeted approach successfully improves performance without introducing additional bias amplification risks compared to standard full fine-tuning.\n\nWe have incorporated this analysis into the revised manuscript. We thank the reviewer for this valuable suggestion, which has strengthened our paper.\n\n> Reference:\n> \n\n[1] European Language Resource Coordination: Collecting Language Resources for Public Sector Multilingual Information Management (LREC 2018)\n\n[2] M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation (NAACL 2024)\n\n[3] Benchmarking Machine Translation with Cultural Awareness (EMNLP 2024)\n\n[4] Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs (EMNLP 2024)\n\nThank you for your thorough review and helpful suggestions. We commit to incorporating the above analysis and experiments into the revision to address all these points."}, {"review_id": "1Bc5JzIjB9", "reviewer": "Reviewer_NbyD", "summary": "This paper presents a framework for analyzing the internal translation mechanisms of large language models (LLMs), with a focus on understanding how these models perform multilingual translation at the computational component level. The work addresses a gap in mechanistic interpretability by moving beyond surface-level observations to analyze the causal relationships between model components and translation capability. The authors propose \"subspace-intervened path patching,\" a refinement of existing path patching techniques that enables more precise causal analysis by identifying and intervening only within task-specific \"translation-steering\" subspaces of component activations, rather than manipulating entire activation vectors. Through systematic application of their method, the authors identify that translation is driven by a remarkably sparse subset of components (less than 5% of attention heads). The authors demonstrate that targeted fine-tuning of only the identified crucial components (64 attention heads, <5% of parameters) achieves performance parity with full-parameter fine-tuning.", "questions": "1. You analyze word-level translation but claim generalization to sentence level based on fine-tuning results. Can you extend your mechanistic analysis to phrases and sentences?\n2. Can you elaborate on why fine-tuning only 64 heads is sufficient for strong translation performance?\n3. You show that targeted fine-tuning achieves similar performance to full fine-tuning, but can you provide more detailed analysis of the trade-offs? Are there specific translation phenomena where targeted fine-tuning consistently underperforms? What's the performance ceiling of this approach?\n4. Do the translation-crucial components emerge from multilingual pre-training data exposure, or do they develop during fine-tuning? \n5. Could your subspace analysis be applied to other tasks beyond translation?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths:\n- The paper introduces subspace-intervened path patching, which represents a methodological advancement over existing path patching techniques.\n- The research questions framework (Which components? What behavioral patterns? Can we improve?) provides good organization.\n\n### Weaknesses:\n- The paper focuses exclusively on word-level translation, which may not capture the complexity of real-world translation scenarios. While the authors claim generalization to sentence-level translation based on fine-tuning results, the mechanistic analysis itself is limited to single-word mappings.\n- The discovery of English-centric intermediate representations is an important insight, but the current analysis is limited to cosine similarity of MLP outputs with English token embeddings, which is only a coarse signal. The paper does not investigate why or how English emerges as a pivot in non-English ↔ non-English translation pairs.", "was_revised": true, "final_justification": "The authors addressed some of my concerns in the rebuttal. I have adjusted the final score accordingly.", "rebuttal": "Dear Reviewer NbyD,\n\nThank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:\n\n> W #1: Mechanistic analysis is limited to word-level translation\n> \n\nWe wish to first clarify that the decision to focus initially on word-level translation was a deliberate methodological choice to isolate the core mechanisms of translation in a controlled environment, a common practice in previous, solid mechanistic interpretability research [1-3]. This avoids confounding variables at the sentence level—such as stylistic variations, non-trivial mapping, and paraphrasing that produce multiple valid translations—which obscure fine-grained causal analysis.\n\nAs you suggested, we try to extend the analysis to the more complex task of sentence-level translation using the WMT23 En→Zh dataset, following the experimental procedures from Section 4.\n\n| En→Zh | Top Crucial Heads (Layer, Head) | Performance Metric Change (lower logits or higher PPL means poorer translation quality) | Performance Drop (Knockout Top-5 Overlapping Heads) | Performance Drop (Knock out Top-5 Sentence-Level Heads) |\n| --- | --- | --- | --- | --- |\n| word-level | (15, 21), (31, 11), **(18, 26)**, (16, 26), (31, 8), (26, 30), **(20, 20)**, **(14, 16)**,… | -4.47% (logits) | -39% | -2% |\n| sentence-level | (20, 11), **(18, 26)**, (14, 7), **(20, 20)**, **(14, 16)**, (14, 13), (22, 26), (28, 18),… | +10.5% (PPL) | -36% | -43% |\n\nThe causal analysis reveals a 46.9% overlap (30 over 64, some examples of overlapped heads are marked in bold text) between the top-64 crucial heads for sentence- and word-level translation, indicating a shared core translation circuit. Ablating five shared heads severely degrades performance on both word-level (-39%) and sentence-level (-36%) tasks. Conversely, ablating five heads crucial only for sentence translation has a negligible impact on word-level performance (-2%) but substantially reduces the sentence-level performance (-43%).\n\nThe behavioral pattern analysis of non-overlapping attention heads reveals their specialization in long-range dependencies and broader source contexts. Conversely, overlapping heads focus on local syntax and translation indicators.\n\n> W #2: Coarse analysis of English-centric intermediate representations\n> \n\nOur paper's primary objective was to first identify and characterize this phenomenon. The use of cosine similarity is a direct and established method for this characterization as used in prior research [4-6].\n\nWe then conducted a new correlation analysis to demonstrate that this English-centricity has a direct and significant impact on translation performance. We measured the Pearson correlation between the representation's cosine similarity to English and the final translation quality (BLEU, chrF, TER) for 12 resource levels and typologically diverse non-English to non-English language pairs. The results shown in the following table reveal a strong correlation and provide compelling evidence that the English-centric intermediate representation is not a superficial phenomenon but a core factor fundamentally linked to translation quality.\n\n| Correlation with English Similarity | BLEU-1 Score | chrF Score | TER Score |\n| --- | --- | --- | --- |\n| Average across 12 language pairs | 0.905 | 0.873 | -0.919 |\n\nTo investigate **why** English emerges as the pivot, we hypothesized that the pivot language corresponds to the dominant language in the model's pre-training corpus. The Llama models analyzed in our paper were pre-trained on a corpus where English is overwhelmingly dominant [7]. In addition, we conduct a parallel experiment on the Qwen2.5, a model known to be pre-trained on a corpus with a predominance of Chinese data[8]. In this context, we found that Chinese, not English, emerged as the pivot language. This preliminary analysis result somehow supports our hypothesis.\n\nTo illustrate **how** this pivot emerges mechanistically within the model, we performed a logits lens analysis aligned with [4-5]. This visualization reveals a progressive transition: representations of source concepts shift towards their English counterparts in the model's intermediate layers before being translated into the final target language. For instance, when translating “车” (Chinese for *car*) to “voiture” (French), the representation explicitly resolves to “car” around layers 19-27 before shifting to “voiture” in the final layers.\n\n> Q #1: Extend mechanistic analysis to sentences\n> \n\nFor the extended mechanistic analysis of sentence-level translation, please refer to the response to W #1.\n\n> Q #2: Why fine-tunig 64 heads achieve strong performance\n> \n\nOur claim that fine-tuning only 64 heads is sufficient for strong translation performance is principled, grounded in causal analysis and empirical validation:\n\n1. Our causal analysis identifies that a small subset of attention heads (64 of 1024) dominantly contributes to translation, accounting for over 80% of the significant logit modifications.\n2. Our ablation studies (Table 3) reveal that fine-tuning 64 heads offers an optimal trade-off, achieving performance comparable to Full SFT at a substantially lower computational cost, as further increases in trainable heads yield diminishing returns.\n\n> Q #3: Concerns regarding targeted SFT\n> \n\nTables 3 and 4 quantitatively detail the trade-offs between translation performance, training efficiency, computational cost, and catastrophic forgetting. Increasing fine-tuned heads incrementally improves performance but proportionally raises memory consumption and training time. Crucially, aggressively tuning excessive heads exacerbates catastrophic forgetting, degrading general capabilities.\n\nError analysis of underperforming case (Zh→En) clusters revealed the top-three error patterns accounting for >70% of significant performance gaps:\n\n- Style/Diction/Idioms: Targeted SFT yields overly literal translations (e.g., \"*drug market*\" vs. correct idiomatic \"pharmaceutical showcase\" for`新冠肺炎对毒品市场的影响`(COVID-19's impacts on the pharmaceutical showcase)).\n- Noisy Data Robustness: Reduced resilience to ambiguous inputs (e.g., misinterpreting `第9草`(Article 9) as \"*9th draft*\" vs. the correct \"Article 9\").\n- Factual Hallucinations: Generating unsupported details (e.g., adding \"*green light*\" to `充电盒未充满电充电指示灯红灯长亮...`(The charging indication light on the charging box is not yet fully charged, the red light is on)).\n\nOur method achieves performance ceiling statistically comparable to full fine-tuning by exclusively tuning the 64 heads most critical for translation with lower computational cost, as demonstrated empirically in Tables 3 and 4.\n\n> Q #4: Discussion of the emergence of translation-crucial components\n> \n\nAs Section 4.4 claimed, translation-crucial components are formed during multilingual pre-training and are subsequently refined by SFT. The following comparative causal analysis supports this claim.\n\n1. A randomly initialized model exhibits an unstructured logit change matrix with no evidence of specialized translation heads. In contrast, the pre-trained LLaMA-2 model develops a sparse set of critical translation heads, representing a statistically significant distributional shift from the random baseline ($p<0.05$, Figure 1). This demonstrates that these functional circuits emerge during the pre-training phase.\n2. The subsequent transition from the pre-trained to the fine-tuned model induces only a minor, statistically insignificant distributional shift ($p>0.05$, Figure 3). This indicates that SFT primarily enhances or slightly adjusts these pre-existing components rather than forming them.\n\n> Q #5: Can subspace analysis be applied to other tasks\n> \n\nOur method is task-agnostic, adaptable to new tasks by constructing task-specific analysis datasets. To demonstrate this versatility, we applied our analysis to multilingual mathematical reasoning, creating counterfactual examples from the MGSM dataset [9]. Following the principles detailed in Section 3, we consider the following examples from our multilingual mathematical reasoning analysis:\n\n- X_f: \"肖恩有五个玩具。圣诞节他从他爸爸妈妈那里各得到了两个玩具。他现在有多少个玩具？请给出数字: \" (\"Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys he has now? Give the number: \")\n- X_cf: \"肖恩有五个玩具。圣诞节他从他爸爸妈妈那里各得到了两个玩具。他现在有多少个玩具？请转述句子: \" (\"Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys he has now? Rephrase the sentence: \")\n\nThe analysis identified a sparse set of crucial heads for this new task (3.95% among all heads), mirroring the trend observed in translation. The top-5 heads (e.g., (11, 8), (12, 22), (6, 22), (18, 12), (4, 31)) induced an average logit decrease of 9.76%. Subsequently, ablating the top-10 heads caused a substantial performance drop of approximately 60% in task accuracy. These results confirm that our method effectively identifies components critical to mathematical reasoning, validating its task-agnostic nature.\n\n> Reference:\n> \n\n[1] Interpreting and Improving Large Language Models in Arithmetic Calculation (ICML 2024)\n\n[2] Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (ACL 2019)\n\n[3] How do Large Language Models Handle Multilingualism? (NIPS 2024)\n\n[4] Do Llamas Work in English? On the Latent Language of Multilingual Transformers (ACL 2024)\n\n[5] How do Large Language Models Handle Multilingualism? (NIPS 2024)\n\n[6] Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space (EMNLP 2022)\n\n[7] Llama 2: Open Foundation and Fine-Tuned Chat Models (Arxiv)\n\n[8] Qwen2.5 Technical Report (Arxiv)\n\n[9] Language Models are Multilingual Chain-of-Thought Reasoners (ICLR 2023)\n\nThank you for your thorough review and helpful suggestions. We commit to incorporating the above analysis and experiments into the revision to address all these points."}, {"review_id": "cvytxt13o8", "reviewer": "Reviewer_QYtu", "summary": "This paper systematically investigates the internal translation mechanisms within Large Language Models (LLMs), addressing the lack of fine-grained interpretability in multilingual translation tasks. The authors introduce a novel analysis framework called subspace-intervened path patching, enabling precise causal analysis of model components. Through this method, they identify a sparse subset (<5%) of attention heads critical for translation, categorized into source heads, indicator heads, and positional heads, whose outputs are integrated by Multi-Layer Perceptrons (MLPs) into English-centric latent representations. Empirical evaluations demonstrate that selectively fine-tuning these crucial components achieves translation performance comparable to full-parameter fine-tuning, with substantial parameter efficiency and generalization benefits.", "questions": "1. Generalization to Sentence-level Translation (Section 4.2, Line 181)\n You state that the identified components can generalize effectively to sentence-level translation. Is this generalization explicitly demonstrated using subspace-intervened path patching experiments at the sentence level, or is this claim solely supported by downstream fine-tuning tasks? If the former, please clarify the experimental setup and provide results; if the latter, explicitly state this limitation.\n2. Clarification of Component Shifts after CPT vs. SFT (Line 221, Figures 1 and 3)\n In line 221, you mention significant distributional shifts in translation-crucial heads after continued pre-training (CPT), whereas minimal changes after supervised fine-tuning (SFT). However, Figures 1 and 3 do not clearly reflect this distinction. Could you provide additional analysis or quantitative measures (e.g., statistical comparisons, explicit annotations) to clearly illustrate and justify this observed difference?\nSpecifically, a direct visual or numerical comparison highlighting exactly which heads significantly shift and by how much would help clarify your claim.\n3. Number and Variety of Prompts:\n You mentioned the prompt example \"English: cloud - 中文: _\" used in experiments. Did you utilize only this one prompt, or did you employ multiple prompts? Could you specify the exact number and variations of prompts used across your experiments?\n4. Criteria for Defining Crucial Components:\n You indicated the importance of components by calculating relative changes and an importance score. Could you clarify explicitly how you determine the threshold to label a component as crucial? Provide precise numeric values or criteria employed to distinguish crucial from non-crucial components.\n5. Statistical Significance of Behavioral Patterns Analysis:\n In Section 5 (Behavioral Patterns Analysis), you analyze attention heads and MLPs behaviors. Could you clarify the exact number of samples used in these analyses? Additionally, are these sample sizes sufficient to establish statistically significant patterns? Please provide statistical justifications or analyses that confirm the robustness of your observations.\n6. Clarification of \"Unembedding Matrix\" (Line 277):\n Could you clearly define what you mean by the term \"unembedding matrix\"? Does it refer to the final projection matrix from hidden representations to vocabulary logits, or does it represent something else? Providing a formal or intuitive definition would enhance clarity.\n7. Selection of Translation Indicator (IND), Source (SRC), and Target (TGT) Tokens:\n In the MLP analysis, how exactly do you select and categorize tokens into translation indicators (IND), source tokens (SRC), and target-language tokens (TGT)? Taking your example prompt explicitly (\"English: cloud - 中文: _\"), please clearly indicate which tokens belong to each category to eliminate ambiguity.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Quality and Rigor:\n The methodological framework is well-developed and rigorously validated through comprehensive experimental setups, including various language pairs and multiple LLM architectures.\n- Significance:\n Identifying a sparse set of critical components with demonstrated generalization across translation directions is significant for both interpretability and practical fine-tuning efficiency.\n- Clarity:\n The paper is clearly structured, with thorough visualizations and detailed analyses that facilitate understanding of complex internal translation mechanisms.\n\nWeaknesses:\n- Novelty and Originality:\n The paper proposes an analytical approach (subspace-intervened path patching) that advances mechanistic interpretability specifically for LLM translation tasks, which is similar to the method used in [1].\n- Scope Limitation:\n The analysis is currently limited to relatively simple, word-level translation tasks. Extending this framework to sentence-level or more realistic translation contexts would significantly strengthen its practical applicability.\n- Open vs. Closed-source Limitation:\n The proposed interpretability method relies on the ability to access and intervene on internal model activations, limiting its direct application to closed-source or proprietary LLMs.\n- Evaluation Breadth:\n Although thorough, evaluations primarily involve translation tasks across limited language pairs. Expanding the set of languages, especially including typologically diverse or low-resource languages, would help validate the robustness and universality of the proposed insights.\n\nReference:\n1. Interpreting and Improving Large Language Models in Arithmetic Calculation", "was_revised": true, "final_justification": "The overall framework and presentation are very similar to [1]. Even though the authors explained where they see the novelty, I don’t find the contribution particularly original. Also, the comparison with [1] is still not detailed enough—while the rebuttal added some experiments, they remain insufficient. Based on this, I will keep my original score.\n\n\nReference:\n1. Interpreting and Improving Large Language Models in Arithmetic Calculation", "rebuttal": "Dear Reviewer QYtu,\n\nThank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:\n\n> W#1: Novelty and Originality\n> \n\nThe proposed subspace-intervened path patching is distinct from the standard path patching method in [1].\n\nStandard path patching[1] intervenes on entire, full-dimensional activation vectors. In contrast, the proposed method intervenes on a task-specific, causally identified low-rank subspace within activations. In detail, by first identifying the task-relevant low-rank subspace responsible for the translation function (detailed in Algorithm 1) and then intervening only within that subspace via projection patching (Algorithm 2), we can isolate and study core translation mechanisms of the model, enabling a far more precise and fine-grained causal analysis. \n\nMoreover, the above novelty and originality are acknowledged by reviewer ByrS \"By combining causal analysis with low-rank subspace decomposition, it proposes a novel path patching method that surpasses traditional activation patching.\" and reviewer NbyD \"The paper introduces subspace-intervened path patching, which represents a methodological advancement over existing path patching techniques.\".\n\nFurthermore, as we discuss in the response to Reviewer NbyD's Q#5, the applicability of the proposed method extends beyond translation, demonstrating its generalizability as a tool for mechanistic interpretability.\n\n> W#2: Scope Limitation\n> \n\nWe wish to first clarify that the decision to focus initially on word-level translation was a deliberate methodological choice to isolate the core mechanisms of translation in a controlled environment, a common practice in previous, solid mechanistic interpretability research [1-3]. This avoids confounding variables at the sentence level—such as stylistic variations, non-trivial mapping, and paraphrasing that produce multiple valid translations—which obscure fine-grained causal analysis.\n\nAs you suggested, we try to extend the analysis to the more complex task of sentence-level translation using the WMT23 En→Zh dataset, following the experimental procedures from Section 4.\n\n| En→Zh | Top Crucial Heads (Layer, Head) | Performance Metric Change (lower logits or higher PPL means poorer translation quality) | Performance Drop (Knockout Top-5 Overlapping Heads) | Performance Drop (Knock out Top-5 Sentence-Level Heads) |\n| --- | --- | --- | --- | --- |\n| word-level | (15, 21), (31, 11), **(18, 26)**, (16, 26), (31, 8), (26, 30), **(20, 20)**, **(14, 16)**,… | -4.47% (logits) | -39% | -2% |\n| sentence-level | (20, 11), **(18, 26)**, (14, 7), **(20, 20)**, **(14, 16)**, (14, 13), (22, 26), (28, 18),… | +10.5% (PPL) | -36% | -43% |\n\nThe causal analysis reveals a 46.9% overlap (30 over 64, some examples of overlapped heads are marked in bold text) between the top-64 crucial heads for sentence- and word-level translation, indicating a shared core translation circuit. Ablating five shared heads severely degrades performance on both word-level (-39%) and sentence-level (-36%) tasks. Conversely, ablating five heads crucial only for sentence translation has a negligible impact on word-level performance (-2%) but substantially reduces the sentence-level performance (-43%).\n\nThe behavioral pattern analysis of non-overlapping attention heads reveals their specialization in long-range dependencies and broader source contexts. Conversely, overlapping heads focus on local syntax and translation indicators.\n\n> W#3: Open vs. Closed-source Limitation\n> \n\nThe study focuses on mechanistic interpretability, which necessitates reverse-engineering neural network computations. Consequently, accessing internal model components is a methodological prerequisite, not a limitation, aligning with standard practice in the field that primarily uses open-source models for foundational, reproducible discoveries [1-3]. Since current closed-source or proprietary LLMs share architectural homogeneity (i.e., the decoder-only Transformer), the proposed method can be directly used to understand these systems.\n\n> W#4: Evaluation Breadth\n> \n\nThank you for your insightful comments, which inspire us a lot to strengthen this work.\n\nWe wish to first clarify that our initial focus on high-resource language pairs was a deliberate methodological choice rather than an incidental weakness, enabling a clear and reliable analytical baseline. High translation accuracy is crucial for isolating and analyzing core translation mechanisms, ensuring findings are not confounded by the noise from inaccurate translations prevalent in low-resource settings.\n\nAs suggested, we extend the analysis to a set of low-resource (sw, bn) and typologically diverse (bn, ar) language pairs. The results, shown in the following table, reveal that the key findings (the sparsity and transferability of key heads) hold across these low-resource and typologically diverse language pairs. These additional results confirm the universality and robustness of the findings. \n\n| Language Pair | Crucial Heads Proportion | Top Crucial Heads (Layer, Head) (overlapped marked in bold text) | Average Logits Change Ratio (the lower, the poorer translation quality) |\n| --- | --- | --- | --- |\n| En-Sw | 2.93% | **(16,26),(31,8),(18,11),(17,25)**,(15,17),… | -6.81% |\n| Zh-Sw | 3.32% | **(31,8),(18,11),(16,26),(17,25),(14,10)**,… | -7.19% |\n| En-Bn | 3.71% | **(30,18),(31,8),(14,10)**,(26,7),(28,20),… | -9.17% |\n| Zh-Bn | 2.34% | **(31,8),(30,18),(18,11),(14,10),(26,7)**,… | -8.20% |\n| En-Ar | 2.83% | (**30,18),(31,8),(14,10),(31,4)**,(20,18),… | -8.20% |\n| Zh-Ar | 2.05% | **(31,8),(30,18),(14,10),(31,4)**,(12,17),… | -8.94% |\n\n> Q#1: Generalization to Sentence-level Translation\n> \n\nThe empirical downstream fine-tuning experiment supports this claim. We fine-tune only the crucial heads detected via subspace-intervened path patching to see whether it will improve the sentence-level translation performance compared to the random head fine-tuning. The result outperforms the random baseline and is competitive with Full SFT, supporting the generalization claim.\n\nTo supplement the empirical result, we also conduct an analysis experiment via the mechanistic view at the sentence level to support the claim, please refer to the response to Weakness#1.\n\n> Q#2: Clarification of Component Shifts after CPT vs. SFT\n> \n\nTo provide rigorous quantitative support for this observations, we analyzed the logits changes induced by SFT and CPT relative to the base model. We performed a Two-Sample Kolmogorov-Smirnov (K-S) test on the overall logits change distributions and also quantified the magnitude of change within the top 32 attention heads.\n\n| Comparison | K-S Test p-value | # Heads significantly shift (change > 1%) | Max logits change |\n| --- | --- | --- | --- |\n| Base vs. SFT | 0.355 | 8 of 32 | 3.12 |\n| Base vs. CPT | < 0.00001 | 17 of 32 | 12.03 |\n\nThe results show that CPT induces a statistically significant distributional shift ($p<0.00001$), while SFT does not ($p=0.355$).\n\n> Q#3: Number and Variety of Prompts\n> \n\nWe conducted experiments using 10 base prompts across 4 structural variation types, with details in Appendix B.2 and specific examples in Table 6.\n\n> Q#4: Criteria for Defining Crucial Components\n> \n\nWe defined crucial components as those inducing a logits change of at least 1.0% (stated on Line 168). This threshold is grounded in both empirical analysis and established literature. Our causal analysis indicates that most attention head contributions fall within a ±1.0% range; therefore, this criterion effectively isolates components with a significant impact beyond baseline noise. Moreover, this data-driven threshold aligns with similar logit-based heuristics used in prior interpretability studies [4, 5].\n\n> Q#5: Statistical Significance of Behavioral Patterns Analysis\n> \n\nWe utilized 100 randomly selected Zh↔En samples in both attention and MLP behavior analysis (stated on Line 266, 287), and it is sufficient to establish statistically significant patterns for two reasons:\n\n1. Aligned with influential studies[2,4], the proposed interpretability approach prioritizes representative examples over quantity through manual inspection to uncover mechanistic behaviors.\n2. As suggested, quantitative analysis of the key pattern (e.g., an attention head focusing on source tokens) shows 81 occurrences in 100 samples (81% consistency). The 95% Wilson score confidence interval [72.0%, 87.9%] exceeds chance (50%), indicating systematicity. A binomial test ($H₀: p=0.5$) rejected the null hypothesis ($p<0.001$), confirming significance.\n\n> Q#6: Clarification of \"Unembedding Matrix\"\n> \n\nThe unembedding matrix, $W_{U} \\in \\mathbb{R}^{d_{\\text{model}} \\times |\\mathcal{V}|}$, is the final linear layer that projects hidden states of dimension $d_{\\text{model}}$ onto the vocabulary space of size $|\\mathcal{V}|$.\n\n> Q#7: Elaboration of Specific Tokens\n> \n\nWe use \"English: cloud - 中文: 云\" as an illustrative example:\n\n- IND: Instructional or structural tokens that frame the translation context but are not part of the source text. Here, IND tokens are 'English', ':', '-', and '中文'.\n- SRC: the input text for translation. Here, the SRC token is 'cloud'.\n- TGT: the translated output. Here, the TGT token is '云'.\n\n> Reference:\n> \n\n[1] Interpreting and Improving Large Language Models in Arithmetic Calculation (ICML 2024)\n\n[2] Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (ACL 2019)\n\n[3] How do Large Language Models Handle Multilingualism? (NIPS 2024)\n\n[4] Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 Small (ICLR 2023)\n\n[5] How to use and interpret activation patching (Arxiv)\n\nThank you for your thorough review and helpful suggestions. We commit to incorporating the above analysis and experiments into the revision."}, {"review_id": "Zj4FXHUYUS", "reviewer": "Reviewer_LEwd", "summary": "The presented paper analyzes what contributes to the translation capabilities in LLMs on a word-level dataset and identifies that only sparse subset of heads (less than 5%) are crucial, they utilize an English latent representation, and that fine-tuning only crucial heads is on par with full weight fine-tuning while preserving other LLM capabilities. They verify their findings on multiple open source models.", "questions": "N/A", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n- Research work on understanding the inner working of LLMs for specific tasks (here machine translation) is crucial for pushing the boundaries for specific use cases.\n- Interesting finding that features get transformed into English-centric latent representations.\n- Valuable finding that fine-tuning merely 64 heads achieves performance parity with full-parameter fine-tuning while preserving general LLM capabilities.\n- Experiments have been conducted on multiple open source LLMs.\n\n\n### Weaknesses\n\n- The analysis operates on a very limited set of language pairs (e.g. Chinese centric) most of which are high resource. It would be interesting to analyze if the same trends (especially the crucial heads and the head classification) hold for low resource languages (e.g. Swahili) or language variants (e.g. `en_GB` / Arabic dialects).\n- The finding that an English latent representation is used is interesting but leaves room for analysis, specifically how are gender/formality processed from a source language that doesn't exist similarly in English? A targeted dataset for these features could help shed some light.\n- [Addressed in limitations]: The findings are currently only conducted on word-level translation, it is unclear how well these findings would generalize to longer payloads e.g. sentence-level, paragraph-level, or document-level translation.\n\n\n### Minor Comments\n\n- In the provided code there are a lot of comments still in Chinese which makes it harder to understand for non-Chinese speaking folks.\n- In the provided code there are some comments that may corrupt anonymity e.g. `freeze_mlps=False,  # recall in IOI paper we consider these \"vital model components\"` which makes it easy to assume that there is author overlap with [\"Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small\"](https://arxiv.org/abs/2211.00593)\n- Related work seems to be missing [Analyzing Context Contributions in LLM-based Machine Translation](https://aclanthology.org/2024.findings-emnlp.876/) (Zaranis et al., Findings 2024) which seems highly relevant.\n- Shouldn't the title include `the` i.e. `Exploring Translation Mechanism of Large Language Models` -> `Exploring the Translation Mechanism of Large Language Models`. As it stands currently it also seems hard to grasp what axis the exploration is for (e.g. fluency, adequacy, internal processing, etc.) so it might be worthwhile to be more precise.", "was_revised": true, "final_justification": "All of the weaknesses have been addressed in the rebuttal through additional experiments and/or modifications.", "rebuttal": "Dear Reviewer LEwd,\n\nThank you for your insightful reviews and comments. We appreciate the time and effort you have put into providing valuable feedback. We would like to address your concerns as follows:\n\n> Weakness #1: Limited analysis of language pairs\n> \n\nWe wish to first clarify that our initial focus on high-resource language pairs was a deliberate methodological choice rather than an incidental weakness, enabling a clear and reliable analytical baseline. High translation accuracy is crucial for isolating and analyzing core translation mechanisms, ensuring findings are not confounded by the noise from inaccurate translations prevalent in low-resource settings.\n\nAs suggested, we extend the analysis to a set of low-resource (sw, bn) and typologically diverse (bn, ar) language pairs. The results, shown in the following table, reveal that the key findings (the sparsity and transferability of key heads) hold across these low-resource and typologically diverse language pairs. These additional results confirm the universality and robustness of the findings. \n\n| Language Pair | Crucial Heads Proportion | Top Crucial Heads (Layer, Head) (overlapped marked in bold text) | Average Logits Change Ratio (the lower, the poorer translation quality) |\n| --- | --- | --- | --- |\n| En-Sw | 2.93% | **(16,26),(31,8),(18,11),(17,25)**,(15,17),… | -6.81% |\n| Zh-Sw | 3.32% | **(31,8),(18,11),(16,26),(17,25),(14,10)**,… | -7.19% |\n| En-Bn | 3.71% | **(30,18),(31,8),(14,10)**,(26,7),(28,20),… | -9.17% |\n| Zh-Bn | 2.34% | **(31,8),(30,18),(18,11),(14,10),(26,7)**,… | -8.20% |\n| En-Ar | 2.83% | (**30,18),(31,8),(14,10),(31,4)**,(20,18),… | -8.20% |\n| Zh-Ar | 2.05% | **(31,8),(30,18),(14,10),(31,4)**,(12,17),… | -8.94% |\n\nWe have incorporated a summary of this extended analysis into the appendix of our revised manuscript to validate the robustness of our findings.\n\n> Weakness #2: More analysis of English latent representation regarding gender/formality\n> \n\nOur manuscript's primary focus was to first identify and characterize this emergent phenomenon. Investigating how the English pivot handles linguistic features without direct English equivalents, such as grammatical gender and formality, is a crucial extension of our work.\n\nPrompted by the reviewer's valuable suggestion, we conducted a targeted preliminary analysis to investigate this. We created datasets for French (fr) and Spanish (es), focusing on two key areas:\n\n1. Gendered Professions: Based on the FBK-MT/gender-bias-PE dataset [1].\n2. Formal vs. Informal Expressions: A curated list of common formal/informal expressions.\n\nSome intuitive instances are presented as follows:\n\n| Profession (English) | French (Masculine) | French (Feminine) | Spanish (Masculine) | Spanish (Feminine) |\n| --- | --- | --- | --- | --- |\n| Actor | Acteur | Actrice | Actor | Actriz |\n| Waiter | Serveur | Serveuse | Camarero | Camarera |\n| Baker | Boulanger | Boulangère | Panadero | Panadera |\n| Nurse | Infirmier | Infirmière | Enfermero | Enfermera |\n\n| Category | French (Informal) | French (Formal) | Spanish (Informal) | Spanish (Formal) |\n| --- | --- | --- | --- | --- |\n| People (man) | un mec | un homme | un tío | un hombre |\n| Car | une bagnole | une voiture | un coche | un automóvil |\n| Work / Job | un boulot | un travail | un curro | un trabajo |\n| Money | le fric | l'argent | la pasta | el dinero |\n\nApplying our analysis from Section 5, we measured both the intermediate representation's similarity to the English pivot and the final translation accuracy. Our findings reveal a critical asymmetry in how these features are processed:\n\n| Language Feature | Avg. Cosine Similarity to English Representation | Translation Accuracy |\n| --- | --- | --- |\n| Gender (Male Professions) | 0.32 | 73% |\n| Gender (Female Professions) | 0.11 | 48% |\n| Formality (Formal Expressions) | 0.31 | 65% |\n| Formality (Informal Expressions) | 0.34 | 69% |\n\nAs the table shows, male-gendered professional nouns are processed effectively, with their representations showing high similarity to the English pivot (0.32) and resulting in high translation accuracy (73%). In contrast, the representations for female-gendered nouns show significantly lower similarity (0.11), which correlates with a dramatic drop in accuracy to 48%. Interestingly, both formal and informal expressions are processed with comparable accuracy, suggesting the model preserves this feature through the intermediate representation.\n\nWe hypothesize this gender-specific failure is due to well-documented biases in large-scale training corpora, where female-gendered terms are less frequent[2]. The model’s reliance on a biased English latent space makes it unable to robustly encode and transmit grammatical gender information that is explicitly marked in the source language but often neutralized in English.\n\nFollowing the reviewer's valuable suggestion, we have incorporated this new analysis and discussion into the revision that strengthens the paper’s contribution.\n\n> Weakness #3: Limited analysis to word-level translation\n> \n\nWe wish to first clarify that the decision to focus initially on word-level translation was a deliberate methodological choice to isolate the core mechanisms of translation in a controlled environment, a common practice in previous, solid mechanistic interpretability research [1-3]. This avoids confounding variables at the sentence level—such as stylistic variations, non-trivial mapping, and paraphrasing that produce multiple valid translations—which obscure fine-grained causal analysis.\n\nAs you suggested, we try to extend the analysis to the more complex task of sentence-level translation using the WMT23 En→Zh dataset, following the experimental procedures from Section 4.\n\n| En→Zh | Top Crucial Heads (Layer, Head) | Performance Metric Change (lower logits or higher PPL means poorer translation quality) | Performance Drop (Knockout Top-5 Overlapping Heads) | Performance Drop (Knock out Top-5 Sentence-Level Heads) |\n| --- | --- | --- | --- | --- |\n| word-level | (15, 21), (31, 11), **(18, 26)**, (16, 26), (31, 8), (26, 30), **(20, 20)**, **(14, 16)**,… | -4.47% (logits) | -39% | -2% |\n| sentence-level | (20, 11), **(18, 26)**, (14, 7), **(20, 20)**, **(14, 16)**, (14, 13), (22, 26), (28, 18),… | +10.5% (PPL) | -36% | -43% |\n\nThe causal analysis reveals a 46.9% overlap (30 over 64, some examples of overlapped heads are marked in bold text) between the top-64 crucial heads for sentence- and word-level translation, indicating a shared core translation circuit. Ablating five shared heads severely degrades performance on both word-level (-39%) and sentence-level (-36%) tasks. Conversely, ablating five heads crucial only for sentence translation has a negligible impact on word-level performance (-2%) but substantially reduces the sentence-level performance (-43%).\n\nThe behavioral pattern analysis of non-overlapping attention heads reveals their specialization in long-range dependencies and broader source contexts. Conversely, overlapping heads focus on local syntax and translation indicators.\n\nIn summary, our analysis demonstrates the generalization of our method and findings to the sentence level. This provides a clearer mechanistic view of the model's translation process and underscores the broader applicability of our results.\n\n> Question #1 & 2: Some comments regarding the code\n> \n\nWe thank the reviewer for their meticulous feedback on our code repository. We have addressed the points raised to improve the clarity and accessibility of our code:\n\n1. Comment Language: To ensure our code is accessible to a wider audience, all comments have been rewritten in English.\n2. Anonymity: We clarify that our implementation adapts public code from the \"Interpretability in the Wild\" (IOI) paper[6]. The reviewer-flagged comment (`# recall in IOI paper...`) is original to that codebase and was retained for attribution. The \"we\" in the comment, therefore, refers to the IOI authors[6], not the authors of this work, posing no risk to the blind review process.\n\nWe can confirm there is **no author overlap**. To prevent any further misinterpretation, we have removed this specific comment and rephrased similar annotations inherited from the original codebase.\n\n> Question #3: Related work\n> \n\nWe thank the reviewer for bringing this valuable work to our attention. We have now cited and discussed Zaranis et al. (2024)[7] in our Related Works section in the revised manuscript as follows:\n\nZaranis et al. analyze context utilization in MT, examining how LLMs employ contextual elements like few-shot examples and source text during translation generation. This work analyzes translation from the view of input contexts, which is fundamentally distinct from our work, which focuses on mechanistic interpretability.\n\n> Question #4: Refinement of the title\n> \n\nThank you very much for your thorough review. We will add the definite article as suggested to improve grammatical precision, revising the title to \"Exploring the Translation Mechanism of Large Language Models.”\n\n> Reference:\n> \n\n[1] What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study (EMNLP 2024)\n\n[2] Gender Bias in Large Language Models across Multiple Languages (TRUSTNLP 2025)\n\n[3] Interpreting and Improving Large Language Models in Arithmetic Calculation (ICML 2024)\n\n[4] Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned (ACL 2019)\n\n[5] How do Large Language Models Handle Multilingualism? (NIPS 2024)\n\n[6] Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small (ICLR 2023)\n\n[7] Analyzing Context Contributions in LLM-based Machine Translation (EMNLP 2024)\n\nThank you for your thorough review and helpful suggestions. We commit to incorporating the above analysis and experiments into our revision to address all these points."}]

Author rebuttal:

Meta-review: This paper proposes a mechanistic interpretability study of the internal translation mechanisms in LLMs to shed light on fine-grained interpretability in multilingual translation tasks. The main contribution is a framework called "subspace-intervened path patching" which extends standard path patching. A sparse subset (<5%) of attention heads is identified which is critical for translation, whose outputs are integrated by Multi-Layer Perceptrons (MLPs) into English-centric latent representations. Empirical evaluations demonstrate that selectively fine-tuning these crucial components achieves translation performance comparable to full-parameter fine-tuning. The main weaknesses pointed out by reviewers are the limited novelty of the  path patching approach over previous existing ones, the analysis being limited to word-level translation tasks (rather than sentence, paragraph, or document-level translation), and the limited set of languages considered. In their rebuttal, the authors convincingly addressed all these points, clarifying the differences and significance of their proposed approach with respect to standard path patching, providing new sentence-level experiments and adding new languages. I therefore recommend acceptance.
Decision: Accept (poster)