Analyzing Multilingualism in Large Language Models with Sparse Autoencoders
Authors: Ikhyun Cho
Julia Hockenmaier
Venue: COLM 2025 (2025)
Keywords: Large Language Models
Multilingualism
Sparse Autoencoders
TL;DR: We provide several distinct patterns between high- and low-resource languages in LLMs through the lens of Sparse Autoencoders

Abstract: Despite the impressive multilingual capabilities of recent large language models (LLMs), the mechanisms underlying their language-specific processing remain largely unclear. In this paper, we investigate how LLMs handle multilingualism through the lens of sparse autoencoders (SAEs), uncovering distinctive patterns that offer new insights into their internal workings. Specifically, we introduce two novel concepts—task instruction–focused (TF) and heading-focused (HF) SAE features—and use them to reveal intrinsic discrepancies between high- and low-performing languages. Our analysis yields several key findings: (1) SAEs provide concrete evidence that LLMs have a precise understanding of prompt structure; (2) heading keywords (e.g., “Question,” “Choices,” and “Answer”) play a distinct role in LLM processing; and (3) low-performing languages exhibit a relative deficiency in TF features compared to high-performing languages.
    
Building on these insights, we propose two practical strategies to improve zero-shot multilingual performance: (1) incorporating English heading keywords and (2) amplifying TF features through steering. Our approach improves zero-shot performance in low-performing languages by up to 3.7% on average on ARC-Challenge and MMLU, while also shedding new light on fundamental differences between high- and low-performing languages in LLMs. Our code is available at https://github.com/ihcho2/SAE-ML.

Published as a conference paper at COLM 2025
Analyzing Multilingualism in Large Language Models with Sparse Autoencoders
Ikhyun Cho & Julia Hockenmaier
Siebel School of Computing and Data Science
University of Illinois at Urbana-Champaige. Illinois, IL 61801, USA (ihcho2, juliahmr weieleeelllleeeeeeeeeeeeee inois. edu
Abstract
Despite the impressive multilingual capabilities of recent large language models (LLMs), the mechanisms underlying their anguage-speciffin process- ing remain largely unclear. In this paper, we investigate how LLMs handle multilingualism through the lens of sparse autoencoders (SAEs), uncover- ing distinctive patterns that offer new insights into their internal workings. Specifically, we introduce two novel concepts task instruction-focusee (TF) and heading-focused (HF) SAE features and them to reveal intrinsic discrepancies between high- and low-performing languages. Our analysis yields several key findings: (1) SAEs provide concrete evidence that LLMs have a precise understanding of prompt structure; (2) heading keywords (e.g., "Question," " "Choices," and " "Answer") play a distinct role in LLM processing; and (3) low-performing languages exhibit relative deficiency TF features compared to tigh-perrorming languages.
Building on these insights, we propose two practical strategies to improve zero-shot multilingual performance: (1) incorporating English heading key- words and (2) amplifying TF features through steering. Our approach im- proves zero-shot performance perrforonnnn languages by up to 3.7% on average on ARC- ERC-Challenge and MMLU, while also shedding new light on fundamental differences between high- and low-performing languages in LLMs. Our code is available at https:/ ggithub.com//hchh2]) SAE-ML.
Introduction
Large language models (LLMs) have revolutionized the field of natural language processing, demonstrating remarkable performance across wide range of tasks. Among these, their multilingual capabilities have attracted significant attention. Extensive research has been dedicated to enhance multilingual performance, such as language-specific finetuning (Ade- lani et al., 2021; Wilie et al., 2020), injecting language capabilities through continual learning (Shi al., 2024; Cahyawijaya et al., 2023), adapter-based fine-tuning (Yong et al., 2022), prompting (Li et al., 2023b), among others. However, despite their impressive performance, the precise underlying mechanisms that enable LLMs to handle multilingual tasks remain elusive. Gaining deeper insights into these processes is crucial, not only for advancing our theoretical understanding of LLMs but also for addressing the persistent performance gaps across different languages (Cahyawijaya et al., 2024).
A promising direction in this space cross-lingual in-context learning (X-ICL) (Winata et al., 2021), which aims to enhance target language performance by transferring knowledge from pivot language, leveraging the robust in-context learning capabilities of LLMs (Brown et al., 2020). This approach is computationally tuning-ant provides a direct means to explore various aspects of multilingualism in LLMs. In this study, extend this line of research by incorporating sparse autoencoders (SAEs), an emerging tool in mechanistic interpretability, to further investigate the multilingual processing mechanisms of L.LMs.
no parameter
1

---

Published as a conference paper at COLM 2025
SAEs, an entirely unsupervised approach, are rapidly emerging key tool in mechanistic interpretability. By using a sparsity loss term to reconstruct input embeddings (i.e., LLM dense embeddings), SAEs have proven effective- to some extent-in decomposing these embeddings into sparse features, each corresponding to human-understandable concepts (Sharkey & Beren, 2022; Bricken et al., 2023). In this work, we demonstrate that this effective tool can yield novel insights into the underlying mechanisms of multilinguallism in LLMs. Specifically, we analyze SAE patterns using parallel corpora, multilingual-ARR (Lai et al., 2023) and multilingualMMMLU (OpenAI, 2024), spanning multiple languages, seeking for distinct, consistent patterns across languages.
Our analysis reveals several key findings:
1. SAEs provide concrete and tangible evidence that LLMs have a precise grasp of prompt structure, effectively distinguishing between headings, task instructions, and test example segments. Our ablation results show that this understanding is impressively accurate.
2. We introduce two types of SAE features) and heading-focusee features (HF features)-and identify fundamen- tal discrepancies in these features between high- and low-performing languages, offering novel insights into their intrinsic differences.
features (TF
3. Specifically, low-performing languages exhibit a relative lack of TTF features com- pared to tigh-performing languages. Notably, we observe strong correlation between the number of TF features and overall performance, highlighting their critical role in language-specific performance.
4. SAEs reveal that headings play distinct role in LLM processing, as evidenced by the presence of multiple features that primarily activate on headings (i.e., HF features).
Based on above findings, we present two practical methods to enhance multilingual perfor- mance:
1. English Heading Prompts: Motivated by the distinctive role of headings in LLMs, we demonstrate that using English headings consistently enhances performance in cow-performing languages. This approach is based on the rationale that, since LLMs are predominantly English-centric and treat English as a pivot language, they can more effectively leverage the critical function of headings when presented in English rather than in low-performing languages.
2. Amplifying TF Features: We show that amplifying the identified English TF fea- tures can enhance performance across both high- and low-performing languages. Notably, this approach not only improves www-erformingg languages but also high- performing languages, verifying the crucial role of TF features in LLMs' overall language performance.
By combining both approaches, we achieve average performance gains of up to 3.70% M-MMLU and 1.94% on m-ARC for low-performing languages.
In summary, our main contribution is the introduction of two new types of SAE features (TF and HF features) which offers fresh insights into the fundamental discrepancies across languages in LLMs.
2 Related Work
2.1 Approaches to Understanding Multilingualism in Large Language Models
Various approaches have been proposed to analyze multilingualism in LLMs. For instance, some studies have explored multilingual reasoning abilities through self-attention layers (Hou et al., 2023; Li et al., 2023a), while others have identified language-specific neurons (Zhao et al., 2024) examined languate-centricity using the decoder-lens methodology
2

---

Published as a conference paper at COLM 2025
(Kargaran et al., 2024; Geva et al., 2022). Another promising and widely adopted approach involves leveraging the robust in-context learning (ICL) capabilities of LLMs (Winata et al., 2021).
In-context learning (ICL) the emerging ability of LLMs to tackle a variety of tasks by leveraging just few exemplars within prompt has become dominant paradigm in natural language processing (Brown et al., 2020). Building on this capability, cross-lingual ICL (X-ICL) (Winata et al., 2021) aims to enhance performance in target language by trans- ferring knowledge from a pivot language. Recent studies have explored various strategies to improve target language performance, which is often low-resource. For example, Tanwar et al. (2023) show that semantically similar exemplars are crucial for effective language transfer, while Winata al. (2022) find that mixing exemplars from multiple languages can also enhance performance. Cahyawijaya et al. (2024) propose in-context query alignment, which selects exemplars most similar to the test query and incorporates their translation pairs to further improve ICL performance in low-resource languages. Zhang et al. (2024) argue that the effectiveness language transfer largely depends on the quality of the prompt template. Additionally, Tu et al. (2025) demonstrate that simply exposing models to multiple languages could sometimes improve performance regardless of the precise context.
While these works predominantly focus the X-ICL framework-a cross-lingual transfer setup in which knowledge is transferred from a pivot language to target language via in-context learning we shift our focus to the zero-shot setting. Our primary objective is not to facilitate transfer, but rather to investigate the fundamental discrepancies between languages. We believe the zero-shot setting is better suited for uncovering these intrinsic differences in LLM behavior while also providing an opportunity to explore an under- studied area.
2.2 Sparse Autoencoders as Key Analytical Tool
Sparse autoencoders (SAEs) have become prominent method for sparse dictionary learning, aiming to break down LLM embeddings into sparse, mono-semantic features (Huben et al., 2024). While they have demonstrated remarkable success in identifying ground truth features in controlled, toy experiment settings (Sharkey & Beren, 2022), applying them to real LLMs presents several challenges. Despite these hurdles, SAEs remain a widely used baseline in mechanistic interpretability due to their simplicity and effectiveness (Lieberum et al., 2024).
SAEs, consisting of an encoder and a decoder, use a squared error reconstruction loss and a sparsity penalty:
1 1 N
LSAE = |
Σ
x00 + + Liparsity
(1)
where (ii) represents the original LLM dense embedding, and 800 is the reconstructed output of the SAE:
( Waec W (Weeee (x(1)))
(2)
For the activation function Ør, various techniques are used, such as JumpReL.U (Lieberum et al., 2024) and TopK-ReLU (Gao et al., 2024). Each column of Waec is designed to capture sparse, mono-semantic concepts, which we refer to as SAE features throughout this paper.
In this study, we leverage SAEs to investigate multilingualism in LLMs, uncovering several intriguing patterns. To the best of our knowledge, this is one of the first works to apply SAEs in the study of multilingualism.
Analyzing Multilingualism in LLMs with SAES
3.1 Motivation
Inspired by the impressive performance sparse autoencoders (SAEs) in mechanistic interpretability, researchers have recently begun applying them range of practical tasks
3

---

Published as a conference paper at COLM 2025
beyond interpretability (Demircan et al., 2024; Wu et al., 2025; Kantamneni et al., 2025). Building on this momentum, we explore their potential in the study of multilinguality. In this work, we demonstrate that SAEs can indeed yield novel insights into multilingual processing as well.
3.2 Our Objective and Approach
Our primary goal is to gain deeper insights into how LLMs process multilingualismm. To achieve this, we take straightforward approach by analyzing SAE-derived patterns across parallel multilingual corpora, focusing on identifying both consistent and distinct patterns between high- and low- now-perfrrming languages. Ultimately, we aim to leverage these findings to improve performance in low-performing languages.
3.3 Proposed Methods
In this work, we present series of findings and discussions on intriguing SAE patterns across languages. We begin by defining two key types of SAE features: Task Instruction- Focused (TF) and Heading-Focusee (HFF) features. Our analysis reveals a consistent dis- crepancy between high- and low-performing languages with respect to these feature types. Building on these findings, we propose two novel approaches for enhancing multilingual performance: English Heading Prompts (EHP) and Amplifying TF features (ATF).
We start with formally defining TF and HF features that we consider particularly interesting, which serve the building blocks for analysis.
Definition of Task Instruction-Focused Features (TF Features) We identify several fea- tures that primarily activate on the task instruction segment and are largely deactivated in other parts of the prompt. We define these as task instruction-focusse tures). Specifically, a feature is considered TF-fcature if the proportion of its activated tokens within the task instruction exceeds p% of all its activated tokens, computed over the ARC-Challes Challennee training set (approximately 1,000 examples). Formally, features that meet the following criterion are defined TF features:
features (TF fea-
1[SAE (X,): > 0]
Vf s.t. Ex
>
(3)
[[SAEERC (X:): 0]
Where the feature index, xN denotes the total number of tokens, Xy indicates the number of task instruction tokens, S SAF denotes the SAE encoder, SAFE (Xi)) represents the activation of feature on the i-th token, and 1 represents the indicator function. We set 0.8 as the default value. An example of an actual TF-feature is shown in Figure 2.
Definition of Heading-Focuus Focused Features (HF-Features) Another notable type of SAE feature we observe those that activate primarily on headings (see Figure 3 for an example). We define these as weading-focusel features (HF features) using a similar definition:
Eieh [[AAE (X,): > 0]
Vfi s.t. Ex
>> p
(4)
TNN L [[SAEFRm (Xi); 0
Where H represents the set of headings, which are three words: "Question", "Choices", and "Answer" in our study (See Appendix A.1 for details).
ENGLISH HEADING PROMPTS (EHP) In Section 4.3, we demonstrate that headings hold unique significance for LLMs, evidenced by the existence of multiple SAE features that activate primarily on headings only (i.e., HF features). This finding suggests that headings play crucial role in LLM processing, leading us to hypothesize that converting headings to English- the primary pivot language for these models -could enhance performance in low--prforming languages. Notably, this approach is highly efficient because headings are typically fixed and do not require domain-ppecific knowledge, eliminating the need to modify or introduce new vocabulary for different test queries. As result, our English
4

---

Published as a conference paper at COLM 2025
Heading Prompt (EHP) can be directly applied across various domains. This simple idea consistently improves performance in both MMLU and ARC especially for low- wow-performing languages. Furthermore, EHP outperforms using entire English task instruction, further validating the valuable role that headings play in LLMs.
AMPLIFYING TF FEATURES (ATF) Several recent studies have highlighted the importance of high-quality task instructions (Srivastava et al., 2024), suggesting that task instructions could be crucial for LLM performance. Building on these findings, as well as our new insights in Section 4.2 that low-performing languages typically lack TF features, propose Amplifying TF Features (ATF), a method that activates TF features derived from the English training set. Formally,
ATF: Vi x; +
Sp(!) LED
55)
Here, X represents the original embedding, DTF denotes the set of chosen English TF features, and (D()) refers to the SAE decoder's 1-th column (or feature). DIF and a are tuned on the validation set.
We demonstrate that ATF can further enhance performance, even for high-resource lan- guages, highlighting the critical role of thorough understanding of task instructions in achieving superior performance.
3.4 Experimental Settings
Datasets We utilize multilingual versions of two widely used multiple-choice question- answering benchmarks: multilngual-MMML (STEM) (OpenAI, 2024) and multilingual- ARC- Challenge (Lai et al., 2023). MMLU (STEM) assesses knowledge across diverse subject areas in STEM, while ARC focuses reasoning based on grade-school science knowledge, assembled to encourage research in advanced iuestton-answering Both are formed as four-option multiple-choice questions, following their original format.
Models and SAEs A key requirement of our study is access to well-trained SAEs. Fortu- nately, Lieberum et al. (2024) and He et al. (2024) provide suites of pretrained SAEs for the Gemma2 and Llama3.1 models. Accordingly, and Llama3.1-88 1e.1--B-IInstree models along with their corresponding SAEs in our experiments. Unless otherwise stated, our primary analysis focuses the middle-layer SAE.
use the Gemma2-2B-IT, Gemma2-9B-IT,
Experimental Details As discussed in the Introduction, we adopt zero-shot setting, which remains relatively underexplored but provides a clearer lens into the fundamental differences in how LLMSs process various languages. The baseline prompt used in our apperiments-cooprisinng a task instruction, headings, and test query-is detailed in Appendix A.1. We employ greedy decoding and accuracy as our primary evaluation metric.
For our language selection, we consider total of ten languages. To ensure sufficient com- parability, we selected languages present in both m-MMLU and m-ARC (e.g., languages appearing only in one dataset, such as Swahili, were excluded). Our final selection includes English (En), German (De), Chinese (Zh), Spanish (Es), French (Fr), Portuguese (Pt), Indone- sian (Id), Arabic (Ar), Hindi (Hi), and Bengali (Bn). They are roughly grouped into two, high- and low-perfogming language groups, based on their downstream performance.
4 Main Findings and Discussions
4.1 SAEs Provide Tangible Evidence that LLMs Have Precise Understanding of the Prompt Structure
Large language models (LLMs) have demonstrated notable performance wide range of tasks, which suggest that they possess a robust understanding of input prompts. However, their sensitivity to even minor prompt modifications (Shivagunde al., 2024; Cho et al.,
5

---

Published as a conference paper at COLM 2025
Figure 1: Can SAEs Effectively Differentiate Between Example and Non-Example Seg- ments in More Challenging Inputs? (We use Gemma2-9B-IT in this experiment). We intentionally added a reminder sentence at the end to assess whether the features can dynamically toggle (i.e,, activate, deactivate, and then reactivate) for non-example segments, thereby demonstrating their flexibility, The prompt consists, in order, of a task instruction, test example query, choice options, and reminder. The figure above shows that multiple features activate in both the initial and final segments, while the test-query portion the middle remains strictly deactivated. These results provide compelling evidence that LLMs have a precise awareness of the structure of the prompt (e.g,, ias-iinstructio--relaeed parts and the test-query components).
2024) raises important questions about what it truly means for an LLM to "understand" prompt prompt-and whether we can obtain more fine-grained evidence of this comprehension beyond downstream task performance (Cho et al., 2025).
In this work, we demonstrate that sparse autoencoders (SAEs) provide compelling evidence that LLMs possess precise understanding of distinct prompt segments. Our findings reveal that LLMs can effectively differentiate among task instructions, main example (i.e., test query), and headings. Figures 2 and 3 illustrate this phenomenon: Figure (b) shows that Feature #1 #14428, classified as an HF feature by our definition, activates primarily on headings across all examined languages, while the bottom of Figure 2 shows that Feature #9349 fires predominantly on task instructions across all studied languages (i.e., presence of these features serves as strong, tangible evidence that LLMs possess an accurate understanding of prompt structure, and that comprehension is, to some extent, universally shared across languages.
a TF feature). The
To further evaluate LLMs' ability to distinguish prompt segments, we conducted a study in which we added sentence irrelevant to the main example (i.e., the test query). Specifically, we inserted a "Reminder" contence-"Malle sure your answer is one of A, B, C, or D."-at the end of the prompt. If the LLM activates features on this non-example segment, along with the previous non-example segments before the test query, it provides stronger evidence of its precision in distinguishing between main example and non-main example segments. In other words, we aim to assess whether the features can dynamically toggle (on, off, and then on again), demonstratig their flexibility. As shown in Fig. 1, Features #333, #7479, #1 #11077, among others (using Gemma2-9B-1 effectively differentiate these segments. The existence of these highly accurate segment-aware features offers valuable insights into how LLMs truly understand the prompt.
4.2 Low-performing Languages Relatively Lack Task Instruction-Foocuse Features
An intriguing type of feature we observe is the task instruction-focused (TF) features, as defined in Section 3.3. To analyze these features, we first compute their statistics and compare the number of TF features across different languages. Specifically, we process all tokens in the ARC training set through the SAE encoder and collect the top 500 most frequently activated features. This top-500 selection ensures that we focus on the most meaningful features, filtering out those that activate only sporadically (e.g,, once in every thousand tokens), which are likely to be less significant. Then, we compute the number of TF features (p=0.8) and the results are shown at the top of Figure 2.
Overall, we observe three key findings regarding TF features:

---

Published as a conference paper at COLM 2025
Ganead 28-77 (0/55)
Linigt lustoc: (Pausoo 08891
Gencnaz $8 PPassurr
Performanc on ARC Challenge
Performance on ARCC CHc--Chlllggg
RC.00
12.12
B.
m_.
13
AR-31 10.0
10.30
Number PTPPPPettree (p-0.))
Number of Fastures (p-n.))
Number (p-n.))
DE DE
2H CN
E8. LA
A Celoos
TR_FR
PT BR
i0_16
1N
Figure 2: Existence of Task Instruction-Focused (TF) Features and Their Correlation with Language Performance: (a) The first three plots at the top row show a strong cor- relation between language performance and the number of TF features. This consistent trend across three different -uggessg that TF features can provide new insights into the causes of performance gaps between languages in LLMs. (b) (Feature #9349 in Gemma2-99BIT)). Although many TF features are multilingual, there is often significant difference their activation frequency: notably, low-performing languages (as shown in the bottom row) have fewer tokens activat- ing these features.
and Gemma2-
TF Features Shed Light on LLM Prompt Processing SAEs are trained a completely unsupervised bag-of-embeddings fashion, with no inherent expectation that TF features would emerge. Thus, their presence-and especially their abundance both surprising and significant, it offers concrete, measurable proof that LLMs can differentiate between task instruction segments and non-task instruction segments within a prompt. This finding provides valuable insights into how LLMs break down and process prompts, and paves the way for promising future research directions.
TF Features Provide Deeper Insights into Language Discrepancies As illustrated in Fig- ure 2 (a), the number of TF features differs significantly between high- and low www-pefforing languages. Additionally, the number of TF features exhibits decent correlation with overall downstream performance (see Figure 2), suggesting that these features could play a crucial role in language performance.
TF Features Are Generally Universal, but Their Activation Frequency Is Lower for Low- Performing Languages The bottom of Figure 2 shows that although Feature #9349 in Gemmaa-99-IT'ss SAE activates across all languages, low-performing languages exhibit lower activation frequency (as indicated in the last row), highlighting subtle discrepancies
7

---

Published as a conference paper at COLM 2025
between high- and low-performing languages (more examples in Appendix A.1). We argue that these consistent patterns- wherein www-prrforming languages lack TF features in both number and frequency-indee indicate that TF features play crucial role in LLMs' multilingual performance.
Overall, the key takeaway is that TF features strong candidates for playing crucial role in the multilingual performance of LLMs.
4.3
The Importance of Headings in LLMs
21,55
(a) Lenient HF Features: #14230
(b) Strict HF-Features: #14428
Figure 3: Existence of Various HF features (using Gemma2-9B-IT), The abundance of these features serves evidence that headings hold special significance for LLMs compared to other general words, indicating that LLMs have a clear understanding and focus on headings when processing the prompt. The figures above suggest that HF features can be further classified into lenient and strict HF features.
Another intriguing class of features we identify is the heading-focusett (HF) features, as defined in Section 3.3. Similar to TF features, the emergence of HF features is surprising, as there no inherent guarantee that they should exist. For example, as shown in Figure 3(b), feature #14428 (using Gemma2-9B-IT consistently activates on headings across multiple languages. The abundant existence HF features provide clear evidence that LLMs possess sophisticated understanding of headings- critical component of prompt structure.
Another noteworthy observation the presence somewhat "noisy" HF features. For instance, Figure (a) shows Feature #14230, which is clearly heading-aware, as it activates strongly on headings, though not as precisely as Feature #14428 in Figure 3 (b). Given that model is perfect, such imperfections in SAEs can be understood. Nevertheless, this suggests that there could be many more HF features than our measurements we also account for the lenient ones. Investigating TF and HF features in more detail would be an interesting area for future work.
4.4 Enhancing Low-performing Language Performance: English Heading Prompts (EHP)
Based on our findings, we have identified multiple HF features, indicating that LLMs place significant emphasis on headings, which play a unique role in their mechanism. Inspired by this observation, replaces headings with English words. This approach is motivated by the fact that LLMs are predominantly English-centric and use English as the pivot language, enabling them to more effectively leverage the critical role of headings when presented in English rather than in low-performing languages.
introduce English Heading Prompts (EHP), method that
The results, summarized in Table 1, show that EHP consistently improves performance across languages, with a relatively larger impact on low-performing languages. These findings empirically validate our hypothesis that headings play crucial role in LLM operations. What is especially noteworthy is that EHP outperforms the use of complete English task instruction (denoted ETI in Table 2). Despite ETI generally containing larger word count in English, EHP proves to be more effective, further underscoring the important role headings play in L.Mss.
8

---

Published as a conference paper at COLM 2025
Task: Multilingual ARC-Challenge
HPL
LPL
Avg.
Gemma2-9B-IT
En
De
Zh
Es
Fr
Pt
ld
Ar
Hi
Bn
HPL Ang.
LPL Avg.
Zero-shot (ZS)
90.30
84.98
85.08
83.75
85.76
83.45
78.54
72.34
57 68
85.72
73,00
ZS EHP
90.30
85.24
85. 16.16
87.31
85.75
86.11
85.08
79.31
73:11
58.80
74.08
ZS EHI ATF
90.82
85.84
85.76
87.65
86.27
86.88
85.51
80.43
73.54
39.91
86.48
74.85
Gemmma2-B--I
En
De
Zh
Es
Fr
Pt
Id
Hi
Bn
HPL Avg.
LPL Avg.
Zero-shot (ZS)
71,93
58.11
62.01
64.41
65.06
63.04
60.12
50.30
48.11
33.73
62.12
44.05
EHP
59.48
64.84
64.38
34.94
44.82
ZS EHI ATF
72.45
60.09
62.52
65.52
64.89
64.15
61.58
51.59
49,91
36.48
63.12
45,99
Llmaa311 1 1-881-1----sstr
17c
Zh
Es
Fr
Pt
Hi
Bn
HPI. Avg.
L.I. Avg.
Zeso-shot (ZS)
72.45
53.91
59.52
58.49
56.22
56.35
49.83
45.24
40.29
31.24
55.72
38.92
EHP
72.45
55.88
59.26
59.52
56.65
51.20
45.67
40.72
30.99
39.13
ZS EHP ATF
73.91
56,22
59.52
60.46
57.08
38.92
51.50
46.35
41.58
31.76
57.28
39,90
Task: Multllngual MMLU (STEM)
Gemma2-99-II
HPL
LPL
Avg.
Zh
Es
Fr
HRL Avg.
Avg.
Zero-shot (ZS)
63.72
57.04
54.93
59.94
58.37
56.26
53.05
48.04
51.02
46.17
57.31
49.57
75 + EHP
63.72
58.92
56.73
60.17
59.31
58.84
57.04
50.86
53.05
48.67
58.79
52.40
EHP+ATF
58.22
60.49
59.75
59.15
53.27
Gemman-2B-IT
Zh
Es
Fr
Pt
HPL Avg.
LPL Avg.
Zero-shot (ZS)
47.18
39.56
40.32
41 41.25
40.29
40.39
39.60
35.49
35.22
30.78
40.23
33.83
ZS + EHP
47.18
40.32
40.95
41. 62
49
41.02
39.30
36 28
35.55
31. 91
40.62
34.58
EHP
41.96
33.53
35.55
LJama3.1-88 In8-strrcct
En
De
Zh
Es
Fr
Pt
1d
Ar
Hi
BB
HPL. Avg.
LPL Avg.
Zero-shot (ZS)
48.71
39.50
40.62
41.82
40.29
40.39
34.29
30.78
40.18
33.31
7S + EHP
48.71
39.43
41.95
41. 88
40.49
41.02
38.30
35.39
34.26
31.91
40.51
33.85
EHP ATF
48.71
39.76
42.28
42.41
40.49
41.58
38.73
36.28
35.49
33.13
40.87
34.97
Table 1: Overall results of our proposed approaches. We observe that our methods consistently improve performance across both high-performing (HPL) and low-performing (LPL) languages.
4.5 Enhancing Multilingual Performance Via Amplifying TF Features (ATF)
In the previous sections, we discovered the existence of multiple TF features and observed distinct behaviors across high- and low-performing languages, along with a strong cor- relation between the TF feature ratio and overall task performance. Motivated by these findings, we introduce a new methodology, "Amplifying TF Features (ATF)" as defined in Section 3.3. In this approach, for each language, first derive TF features from the English ARC-Challenge training set using equation (3). We then randomly select a subset of these TF features for steering. The number of selected features |DrF), the specific TF features chosen, and the value of & are all tuned on the validation set of the target language.
The results in Table indicate that ATF is effective for both low- and herh-peerorming languages. We believe these findings provide concrete evidence that TF features play crucial role in LLMs' performance, an intriguing finding that offers novel insight into the multilingual capabilities of LLMs.
4.6
An Additional Interesting Type of SAE Features Not Covered in This Work, but Worth Noting
We also identify features that may contribute to label bias LLMs. For instance, Feature #4306 predominantly activates on headings and alphabetic answer choices but consistently

---

Published as a conference paper at COLM 2025
Model: Gemmaa-99-I1
Task: Multilingual ARC-Challenge
HPL
LPL
Avg.
De
Fr
Pt
Id
Bn
HPL Avg.
LPL Avg.
Zero-shot (ZS) ZS EHP
84.98
85.08
87.05
85.75
85.76
83.45
78.54
72.34
57.68
85.72
73.00
85.24
85,16
87.31
85.75
86.11
85.08
79.31
73.11
58.80
85.93
74.08
Abl 1) ZS+ ETI
84.64
84.73
85.75
86.22
83.79
79.14
57.94
85.58
73.15
Abl ZS EHP ETI
84.64
85,16
86,79
85.84
86.22
84.22
79.14
72.68
58.11
85.73
73.54
Task: Multilingual-MMMM (STEM)
HPL
LPL
Avg.
De
Pt
Avg.
Avg.
Zero-shot (ZS) ZS EHP 1) 7S ETI
57.04
54.93
59.94
58.37
56.26
53.05
48.04
51.02
46.17
57.31
49.57
58.92
56.73
60.17
59.31
58.84
57.04
50.86
53.05
48.67
58.79
52.40
58.84
57.43
58.76
55.01
50.94
50.79
Ab1 2) ZS EHP ETI
58.84
57.20
60.09
59.23
59.23
55.87
51.49
52.58
45.85
58.92
51.45
Table 2: Ablation results on the distinctiveness of headings (using Gemma2-99-ITD,, Using English headings in the prompt generally improves performance, and interestingly, this often more effective than using the entire English task inrruutiooniidicattin that headings play a unique and powerful role LLMs.
shows zero activation for 'A' alone (see Figure 6), regardless of the specific example. We believe this bias could contribute to the well-known label-bias issue LLMs. Suppressing these features by steering may help mitigate this problem, and we leave this promising direction for future work.
Conclusion
This paper investigates whether sparse autoencoders (SAEs) can enhance understanding of how large language models (LLMs) handle multilingualism. We introduce two novel types of SÀE features: Task Instruction-Focused (TF) features and Heading-Fooe Focused (HF) features. Our analysis reveals several novel insights: (1) TF and HF features shed light on how LLMs parse and process prompt structures; (2) headings play particularly important role in prompts, crucial for multilingual performance. Based on these findings, we propose two practical ap- proaches to enhance multilingual performance: English Heading Prompts and Amplifying TF Features, which consistently enhances performance, especially for low-performing lan- guages. To the best of our knowledge, this work is among the first to explore the versatility of SAEs in multilingualism, highlighting their potential and offering fresh insights into their underlying mechanisms.
evidenced by the abundance of HF-features; and (3) TF features are
Acknowledgements
This research was supported in part by Other Transaction award HR0011249XXX from the U.S. Defense Advanced Research Projects Agency (DARPA) Friction for Accountability in Conversational Transactions (FACT) program. Additionally, this research used the Delta advanced computing and data resource which is supported by the National Science Foundation (award OAC 2005572) and the State of Illinois. Delta is joint effort of the University of Illinois Urbana-Chann -haaaaaaa and its National Center for Supercomputing Applications.
References
David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D'souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, et al. Masakhaner: Named entity recognútion for african languages. Transactions of the Association for Compulational Linguistics, 9:1 9:1116-1131, 2021.
10

---

Published as a conference paper at COLM 2025
Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Con- erly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, Robert Lasenby, Yifan Wu, Shauna Kravec, Nicholas Schiefer, Tim Maxwell, Nicholas Joseph, Zac Hatfield- Dodds, Alex Tamkin, Karina Nguyen, Brayden McLean, Josiah Josiah E Burke, Tristan Hume, Shan Carter, Tom Henighan, and Christopher Olah. Towards monosemanticity: De- composing language models with dictionary learning. Transformer Circuits Thread, 2023. https:/ https///taanffome--ccccuuuu httiiiiiiittuuub///... /203//222///// aatnoommmani-fftttuur/nnddd...ed
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
Samuel Cahyawijaya, Holy Lovenia, Tiezheng Yu, Willy Chung, and Pascale Fung, Instruc- talign: High-and-low resource language alignment via continual crosslingual instruction tuning. arXiv preprint arXiv:2305.1360 13627, 2023.
Samuel Cahyawijaya, Holy Lovenia, and Pascalee Fung. Lims are few-shot in-context low- resource language learners. arXiv preprint arXiv:2403.16511 2024.
1khyun Cho, Gaeul Kwon, and Julia Hockenmaier. Tutor-icl:
Guiding large language
models for improved in-context learning performance. In Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 9406,9506, 2024.
Ikhyun Cho, Changyeon Park, and Julia Hockenmaier. The power of bullet lists: A simple yet effective prompting approach to enhancing spatial reasoning in large language models, In Findings of the Association for Computational Linguistics: NAACL 2025, pp. 3047-3057, 2025.
Can Demircan, Tankred Saanum, Akshay K Jagadish, Marcel Binz, and Eric Schulz. Sparse autoencoders reveal temporal difference learning in large language models. Xrv preprint XxXi::241000120, 2024.
Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. ar Xiv preprint ar Xrxi:2446.0000 04099, 2024.
Mor Geva, Avi Caciularu, Kevin Ro Wang, and Yoav Goldberg, Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space. ar Xiv preprint 1XXiv:2033.16600 2022.
Zhengfu He, Wentao Shu, Xuyang Ge, Lingjie Chen, Junxuan Wang, Yunhua Zhou, Frances Liu, Qipeng Guo, Xuanjing Huang, Zuxuan Wu, et al. Llama scope: Extracting millions of features from Ilama-3.1-8b with sparse autoencoders. arXiv preprint ar XrXi:22400.25526, 2024.
Yifan Hou, Jiaoda Li, Yu Fei, Alessandro Stolfo, Wangchunshu Zhou, Guangtao Zeng, Antoine Bosselut, and Mrinmaya Sachan. Towards a mechanistic interpretation of multi- step reasoning capabilities of language models. ar Xiv preprint ar arXio:2310.1443 2023.
Robert Huben, Hoagy Cunningham, Logan Riggs Smith, Aidan Ewart, and Lee Sharkey, Sparse autoencoders find highly interpretable features in language models. In The Twelfth International Conference on Learning Representations, 2024. URL https: https:///penrrviiew.. net/ 76wwRLLLL
Subhash Kantamneni, Joshua Engels, Senthooran Rajamanoharan, Max Tegmark, and Neel Nanda. Are sparse autoencoders useful? case study in sparse probing. ar arXit preprint arXiv:2502.16681, 2025.
Amir Hossein Kargaran, Ali Modarressi, Nafiseh Nikeghbal, Jana Diesner, François Yvon, and Hinrich Schütze. Mexa: Multilingual evaluation of english-centric 1mms via cross- lingual alignment. Xiv preprint arXit=2410.05873, 2024.
11

---

Published as a conference paper at COLM 2025
Viet Dac Lai, Chien Van Nguyen, Nghia Trung Ngo, Thuat Nguyen, Franck Dernoncourt, Ryan A Rossi, and Thien Huu Nguyen. Okapi: Instruction-tuni tuned large language models in multiple languages with reinforcement learning from human feedback. ar preprint ar Xiv::2300 11.-29 2023.
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inference-time intervention: Eliciting truthful answers from language model. Advances in Neural Information Processing Systems, 36:41451-41533 2023a.
Shuang Li, Xuming Hu, Aiwei Liu, Yawen Yang, Fukun Ma, Philip Yu, and Lijie Wen. Enhancing cross-lingual natural language inference by soft prompting with multilingual verbalizer. Xiv preprint ar xrX:::2305.12761 2023b.
Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, János Kramár, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. ar preprint arXiv:2408.05147, 2024.
OpenAl. Multilingual mmlu dataset. Hugging Face, 2024. URL htttt::tt///ggttee https:///uugggaaaaacee co/ dat datasetssoopeaii/MMi /MMMUU
Lee Sharkey and Dan Braun Beren. [interim research report] taking features out of superpo- sition with sparse autoencoders,, 2022.
Haizhou Shi, Zihao Xu, Hengyi Wang, Weiyi Qin, Wenyuan Wang, Yibin Wang, Zifeng Wang, Sayna Ebrahimi, and Hao Wang. Continual learning of large language models: A comprehensive survey. ar Xiv preprint ar arXim:2404.166 16789, 2024.
Namrata Shivagunde, Vladislav Lialin, Sherin Muckatira, and Anna Rumshisky. Decon- structing in-context learning: Understanding prompts via corruption. arXiv preprint arXiv:2404.02054, 2024.
Pragya Srivastava, Satvik Golechha, Amit Deshpande, and Amit Sharma. Nice: To optimize in-context examples or not? arXiv preprint arXiv:2402.06733, 2024.
Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, and Tanmoy Chakraborty. Multi- lingual ilms are better cross-lingual in-context learners with alignment. arXiv preprint arXi::330.00594, 2023.
Yilei Tu, Andrew Xue, and Freda Shi. Blessing of multilinguality: A systematic analysis of multilingual in-context learning. arXiv preprint Xio:2502.111644, 2025.
Bryan Wilie, Karissa Vincentio, Genta Indra Winata, Samuel Cahyawijaya, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, et al. Indonlu: Benchmark and resources for evaluating indonesian natural language under- standing. ar preprint ar xrXe:2209.003887 2020.
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. Language models are few-shot multilingual learners. arXio preprint arXit:2109.0766 09844,9097844..00 2021.
Genta Indra Winata, Shijie Wu, Mayank Kulkarni, Thamar Solorio, and Daniel Preotiuc- Pietro. Cross-lingual fhot learning on unseen languages. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. pp. 777-791, 2022.
Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D Manning, and Christopher Potts. Axbench: Steering llms? even simple baselines outperform sparse autoencoders. aiv preprint ar Xi10225011.17143 2025.
12

---

Published as a conference paper at COLM 2025
Zheng-Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, Alham Fikri Aji, David Ifeoluwa Adelani, Khalid Almubarak, M Saiful Bari, Lintang Sutawika, Jungo Kasai, Ahmed Baruwa, et al. Bloom+ 1: Adding language support to bloom for zero-shot prompting. ar preprint arXim:2212.09532 2022.
Miaoran Zhang, Vagrant Gautam, Mingyang Wang, Jesujoba O Alabi, Xiaoyu Shen, Dietrich Klakow, and Marius Mosbach. The impact of demonstrations on multilingual in-context learning: A multidimensional analysis. arviv preprint arXio:2402.12976 2024.
Yiran Zhao, Wenxuan Zhang, Guizhen Chen, Kenji Kawaguchi, and Lidong Bing. How do large language models handle multilingualism? Xiv preprint ar arXio:2402.18811 2024.
A Appendix
A.1 Prompts Used in Our Study
Baseline Prompt
[Tisk Instruction Lunguage X]
["Question" Language X]:
[Test Query]
["Choices" in Language X]:
A:
[Option A in Language X]
B:
[Option Language X]
C:
[Option C in Language X]
D:
[Option D in Language X] [" ["Answer" Language X):
Figure 4: Baseline Prompt. The baseline prompt used in our study. Headings are highlighted in blue.
The baseline prompt we use in our study is shown in Figure 4 (a). The headings are highlighted in blue, and each square bracket pair indicates placeholder based the test query and language.
A.2 More Examples of SAE Features
In Section 4.2, we noted that even when a TF-feature is multilingual, the frequency of activated tokens is generally lower in low-resource languages, indicating clear distinction between high- and low-resource languages. Here, we present another such feature in Figure 5 along with corresponding statistics using the ARC dataset.
In Section 4.6, we introduced another potentially significant feature type that may contribute to label bias. Examples of these features are illustrated in Figure 6.
13

---

Published as a conference paper at COLM 2025
Mctvtla Oeffreen
Attivetes Ceffboeet
Owffainnt
DR DE
ZIE CN
DS LA
Actwtken Corlain
Nctisados Coeffiion
PR
BR
JD
Astretto Certteyent
AR
BN
Figure 5: Decline in TF Feature Frequency for Low-performing Languages. We provide another example of TF-features showing a significant reduction in activation frequency for low-resource languages (see the last row).
Quisten
Quodon
Natertin CAITORE
DI
Z1Í CN
ÎD ID
Duseitoon
Clas
Aboees
Osisition
C503045
300W00
Ouestioo
Chriegs Melrec
Activatan Cartooe
Activettnn Cattiiett
ARR XY
BN RDD
Figure 6: Features that may contribute to label bias: Feature #4306 exhibits behavior similar to HF-features, as it strongly activates on headings. However, we observe a consistent pattern where its activation for 'A' is significantly lower than for 'B, 'C, and 'D' across most examples.
14


Reviews:
[{"review_id": "rNJoX0nYoK", "reviewer": "Reviewer_mB9c", "summary": "In this paper the authors explore multilingual capabilities of LLMs using sparse autoencoders (SAE). They introduce task instruction-focused (TF) and heading-focused (HF) SAE features and leverage these to study differences in zero-shot performance across 9 different languages. Building on these two features they introduce to techniques to potentially increase performance by incorporating English keywords in headings and by amplifying the introduced TF-features.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper is clear and well-written\n2. The authors use SAE to introduce two concepts and then use these to sucessfully increase performance on zero-shot ARC and MMLU\n3. The method seems to work across multiple languages\n4.  The introduced TF and HF-features seem to have interpretability value according to their experiments", "reasons_to_reject": "1. The paper claims to improve performance on low resource languages, while not actually dealing with any low resource language. Arabic has almost 500 million speakers worldwide, Hindi has more than 600 million, Indonesian 252 million and Bengali around 284 million. For comparison, one of the high resource languages used by the authors, German, has around 180 million speakers\n2. The two benchmarks used are translated for English, which could explain why the English Heading approach worked so well, however this is never mentioned or addressed by the authors\n3. The authors also failed to mention recent criticism on SAE approaches and how this affects their work\n4. The study by design is only limited to one model, which makes the conclusions of the study difficult to translate to other models", "questions_to_authors": "1. Please find another notation for the activation of feature $t$, I don't know if this was on purpose, but $S_E(X_i)_t$ is not appropriate as a notation\n2. While not currently published, there has been some recent criticism on SAE: https://deepmindsafetyresearch.medium.com/negative-results-for-sparse-autoencoders-on-downstream-tasks-and-deprioritising-sae-research-6cadcfc125b9. Could you mention how this impacts the results of your work?\n3. Please mention the languages that you're working on from the beginning of the paper, not in page 5. And also avoid calling Arabic, Hindi, Bengali and Indonesian low-resource languages. This is not true. Something like low-performance languages (at least for your selected tasks) would be more appropriate\n4. Could you expand the discussion in the limitation section about your study only working with Gemma2? How do you think your study translates to other models?", "extra_scores": {}}, {"review_id": "7XEI8b98c8", "reviewer": "Reviewer_UmKf", "summary": "This paper explores how LLMs handle multilingual input, using sparse autoencoders as a tool for interpretability. The authors introduce two new feature types—task instruction–focused (TF) features and heading-focused (HF) features—that emerge in the sparse representations of LLM embeddings. These features reveal how models parse prompt structure, especially across languages with varying resource levels.\nA key insight is that low-resource languages tend to lack TF-features, which correlates with their lower downstream performance. To address this, the authors propose two lightweight interventions:\n\n(i) English Heading Prompts (EHP) – swapping out native-language headings like “Question” or “Choices” with their English counterparts, \n(ii) Amplifying TF-Features (ATF) – boosting the influence of known TF-features in the model’s hidden states.\n\nTogether, these methods improve overall zero-shot performance and specifically by up to 3.7 points on multilingual MMLU and 1.85 on ARC in low-resource languages scenarios.\n\nWhile the results are promising, some methodological choices raise open questions that merit more discussion.\nFirst, the TF-features are derived only from the ARC training set but are also used to steer model behavior on m-MMLU, a dataset with notably different prompt structure and domain content. This constitutes an implicit form of cross-task feature transfer, which the paper doesn’t acknowledge or validate. Without confirming that the same features remain meaningful in m-MMLU, the effectiveness of ATF across tasks rests on a potentially fragile assumption.\n\nSecond, unless I’ve missed something, ATF is never evaluated in isolation. The reported gains always occur alongside EHP, making it hard to assess how much amplification alone contributes. Since the paper positions TF-features as central to performance gaps, a standalone ATF analysis would help clarify their actual impact.\n\nFinally, the paper assumes structural consistency in how LLMs parse prompts across languages, but doesn’t explore how sensitive these features are to prompt phrasing or localization style. Given the variability in multilingual prompt design, this could be an important point.\n\nThese remarks don’t undermine the core contributions, but they do suggest that more careful handling of feature generalization and ablation completeness would strengthen this paper.\n\n\nI found this paper well written, but a bit dense at times, so to conclude, I suggest this paper to be accepted.\n\nNotes: \n* some of the parameters choice seem a bit arbitrary (eg. see section 4.5, why |DTF| was set to 3, why the alpha was set between 50 and 500 ?\n* a contemporary paper just came out with a similar use of SAEs, maybe in a final version, you could discuss it?\nhttps://arxiv.org/pdf/2505.05111\n(Deng et al, 2025/05/08)", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Well written paper, original use of SAEs for multilingual analysis\n- Good experiments design that lead to convincing results\n- Practical interventions with measurable gains", "reasons_to_reject": "None that I can see, I would have just liked to see:\n- more elements regarding the impact of the ATF technique \n- a discussion on the sensitivity of the features to prompt variability (monolingual and multilingual)\n- a discussion on the cross-task generalization you've implicitely done with the TF-Features extracted from ARC and applied to m-mllu  (the last two questions are ofc linked)", "questions_to_authors": "the questions are pretty much self-explanotory given the above but here we go:\n- Why didn't you perform an experiment to assess the impact of ATF alone.\n- what can you tell on the fact that you extracted the TF-features from ARC and applied to another data set?", "extra_scores": {}}, {"review_id": "HxgLxJvI9c", "reviewer": "Reviewer_1FdK", "summary": "The paper applies sparse auto-encoders (SAEs) to interpret LLM embeddings in a cross-lingual\ncontext. It introduces the concepts task instruction-focused and heading-focused SAE features and\napplies them to interpret differences between high-resource and low-resource languages. The\nmethod improves zero-shot performance in low-resource languages according to multilingual-MMLU.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper is very well written. The method Amplifying Task-Focused features improves\nperformance in both low-resource and high-resource languages. The paper helps to\nexplain the gap between low-resource and high-resource languages by showing that\nLLMs have relatively fewer task-focused features for low-resource languages. Although\nonly one model is used, a total of 9 languages are examined.", "reasons_to_reject": "The primary limitation is in the breadth of experiments: one LLM is tested, the\nexperiments are performed in a zero-shot context, and only one layer of the LLM is\nexamined.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xNjLFXtcs3", "reviewer": "Reviewer_BE52", "summary": "This work performs analysis of crosslingual transfer in Gemma2-9b models via Sparse Auto Encoders. It identifies several important features for the model which they refer to as \"Task Instruction focus\" and \"Heading focus\" and demonstrate that these features are activated unevenly across languages, which impacts the final performce in lower-resource languages. Based on these findings they propose 2 strategies to improve performance in lower-resource languages: usage of English headings in the prompt and amplification of Task instruction features. They demonstrate that both of these techniques allow to improve performance on lower-resource languages.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- this work performs interesting analysis of crosslingual knowledge transfer in Gemma 2-9B models with Sparse auto encoders\n- it provides some insights on what are good \"prompt formulation\" would be to favour knowledge transfer, which is important for less resourced languages. these insights could easily be applied to other models (althought this work does not provide evidence that it would transfer beyond gemma-2-9b models)\n - Task-focus feature amplification is an interesting strategy, and could be used for other features identified by SAE in the future (Eg. authors mention the \"answer position bias feature\" that they leave for the future work)", "reasons_to_reject": "(rather weaknesses than reasons to reject)\n- the conclusions in this work might not hold beyond gemma 2 models, I believe it is very specific in the way that multilinguality is taken into account during model training. It should be easy to check an impact of English headings and English Task instructions in other models which was not done in this work\n- some of the  languages that are reffered as low-resource in this work are not so low-resource , it would be good to exlicit the criteria for how authors define \"low resource\"\n- one of the findings of this work is that it is better to have prompt headings (eg. \"Question:\", \"Answer:\", etc.) in English rather than in the target language. It seems quite unnatural to translate those headings (if I were to perform these experiments , I would keep them in English), but it is good to have explicit confirmation for this.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "f4CbuVOoX7", "reviewer": "Reviewer_ZoVv", "summary": "This paper investigates the mechanisms underlying multilingual processing in LLMs using SAEs. The authors introduce two novel concepts based on SAE feature activations: Task Instruction-focused (TF) features, which primarily activate on task instructions, and Heading-focused (HF) features, which activate on prompt headings (like \"Question\", \"Answer\"). Their analysis, conducted on Gemma2-9B using pre-trained SAEs in a zero-shot setting on m-MMLU-STEM and m-ARC benchmarks, reveals several key findings: (1) SAEs provide evidence that LLMs have a precise understanding of prompt structure, differentiating instruction, query, and heading segments. (2) Low-resource languages exhibit a significantly lower ratio and activation frequency of TF-features compared to high-resource languages, correlating with downstream performance. (3) Headings play a distinct role, evidenced by abundant HF-features. Based on these insights, the paper proposes two methods to enhance multilingual performance, especially for low-resource languages: (a) English Heading Prompts (EHP), replacing native language headings with English ones, and (b) Amplifying TF-Features (ATF), steering the model by adding activations of identified TF-features. Combining these methods yields performance improvements of up to 3.7% on average for low-resource languages on MMLU.", "questions": "", "limitations": "", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The paper introduces a novel approach by leveraging SAEs, a tool primarily used for monolingual interpretability, to gain insights into the challenging and important area of LLM multilingualism. Identifying specific feature types (TF/HF) linked to prompt structure and language resource level is a significant conceptual contribution.\n- The study provides compelling evidence, grounded in SAE feature activations, for how LLMs might parse and understand prompt components differently across languages. The finding that low-resource languages have a relative deficiency in TF-features offers a potential explanation for performance gaps and is an interesting observation. The identification of dedicated HF-features highlights the structural importance of headings in prompts.\n- The paper proposes two concrete methods (EHP and ATF) derived directly from the analytical findings. EHP is simple and efficient, while ATF demonstrates the potential of feature steering for multilingual improvement.", "reasons_to_reject": "- The findings and methods are demonstrated only on one model (Gemma2-9B) and its specific pre-trained SAEs (primarily Layer 35, 16k width). It's unclear how well these findings (existence/ratio of TF/HF features) and the effectiveness of EHP/ATF generalize to other model architectures (e.g., Llama, Mistral), model sizes, other layers, or different SAE training methodologies/hyperparameters. This limitation is acknowledged but remains significant.\n- The definition of TF/HF features relies on a specific activation threshold (p=0.9). The paper does not explore how sensitive the identification of these features and the subsequent analysis are to this threshold. Similarly, the ATF method involves hyperparameters (|DTF|, alpha) chosen based on validation performance, but more analysis on their selection or stability would be beneficial.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lBuHR3ml1j", "reviewer": "Reviewer_Bq9B", "summary": "The paper investigates the Gemma 2 9b model using sparse autoencoder (SAE) features from the perspective of multilinguality via multilingual multiple choice tasks (m-MMLU-STEM and m-ARC).  \nThe paper argues that the presence of 'active' SAE features correlate well with the prompt structure.  \nSpecifically, the authors distinguish Task Instruction-focused (TF) and Heading-focused (HF) features.  \nThe presence of such features are contrasted between low and high resource languages.  \nAs TF-features are less frequent for lower-resource languages, the paper also proposes a technique for their amplification.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Investigating the multilingual capabilities of LLMs is a timely topic, for which a simple solution is provided in the paper.", "reasons_to_reject": "There are certain design choices in the experimental protocol that are not well justified, with a potentially strong effect on the core findings of the paper.  \nFor instance, the definition of TF (and HF) features have the limitation that it can assign high scores for SAE features that are active very infrequently.  \nThere is a heuristic implemented in §4.2 (i.e. considering only the 500 most frequently activated features) to mitigate the problem, however, there was an easy and more principled fix of considering the (normalized) pointwise mutual information between the presence of a certain property (a token being task-instruction focused) and a particular SAE feature being active as it have already been proposed for sparse contextualized representations in prior work.  \nThe threshold for selecting a SAE feature is not justified either, nor it is discussed why the MMLU dataset was constrained for STEM-related questions.  \nAdditionally, the classification of the tested languages into low and high resource is lacking, which is problematic, as certain findings are based on that distinction.  \nFinally, it would contextualize the results better if results for English were also reported, as this could help in seeing the contrastive difference of the behavior of the SAE features between the language the model was primarily created for and additional target languages.", "questions_to_authors": "How exactly were the languages classified into low and high resource languages?  \n\nComments:  \nThe bar for ZH_CN is absent from Figure 2 (a).  \nIn §3.1 (lines 130--132), it was strange to see (Kantamneni et al., 2025) being cited for supporting the ``remarkable performance'' of SAEs, whereas that paper is rather critical towards the usefulness of SAEs.", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper investigates how large language models (LLMs) handle multilingual prompts by leveraging sparse autoencoders (SAEs) to interpret model behavior. The authors introduce two new feature types: task instruction–focused (TF) and heading-focused (HF) features, which help explain performance differences across languages, especially between high- and low-resource ones. They propose simple methods like using English headings and boosting TF features, which improve performance on zero-shot tasks like ARC and m-MMLU across nine languages. While the study focuses on one model (Gemma 2.9B), the findings offer useful insights into prompt design and could be applied more broadly in future work.

Overall, the reviewers agree that the paper is well-written and presents practical applications across multiple languages, achieving performance improvements of up to 3.7% in low-resource settings. The analysis is clearly presented and demonstrates that sparse autoencoders (SAEs) can offer valuable insights into language prompt structure.

Given the quality of the work and its potential impact, I recommend the paper for acceptance. I encourage the authors to thoughtfully address the reviewers’ comments to further enhance the strength and clarity of the paper.

Possible improvements:
- Improve the clarity of the figures (the texts on Figures 1 and 2 are too hard to read).
- Fix the notation.
Decision: Accept