Interpreting the Repeated Token Phenomenon in Large Language Models
Authors: Itay Yona
Ilia Shumailov
Jamie Hayes
Yossi Gandelsman
Venue: ICML 2025 poster (2025)
Keywords: Attention Sinks
LLM
Repeated tokens
First Token
LLMs
Privacy
Security
Primary area: deep_learning->large_language_models
TL;DR: We found the reason why LLMs struggle with repeating the same word many times and propose a way to fix it.

Abstract: Large Language Models (LLMs), despite their impressive capabilities, often fail to accurately repeat a single word when prompted to, and instead output unrelated text. This unexplained failure mode represents a *vulnerability*, allowing even end users to diverge models away from their intended behavior. We aim to explain the causes for this phenomenon and link it to the concept of "attention sinks", an emergent LLM behavior crucial for fluency, in which the initial token receives disproportionately high attention scores. Our investigation identifies the neural circuit responsible for attention sinks and shows how long repetitions disrupt this circuit. We extend this finding to other nonrepeating sequences that exhibit similar circuit disruptions.  To address this, we propose a targeted patch that effectively resolves the issue without negatively impacting the overall performance of the model.  This study provides a mechanistic explanation for an LLM vulnerability, demonstrating how interpretability can diagnose and address issues, and offering insights that pave the way for more secure and reliable models.

Interpreting the Repeated Token Phenomenon in Large Language Models
Itay Yona Ili Shumailov Jamie Hayes'
Yossi Gandelsman"
Abstract
has remained an open question up until now.
Large Language Models (LLMs), despite their impressive capabilities, often fail to accurately re- peat single word when prompted to, and instead output unrelated text. This unexplained failure mode represents vulnerability, allowing even end users to diverge models away from their in- tended behavior. We aim to explain the causes for this phenomenon and link it to the concept of "attention sinks", an emergent LLM behavior crucial for fluency, in which the initial token re- ceives disproportionately high attention scores. Our investigation identifies the neural cireuit sponsible for attention sinks and shows how long repetitions disrupt this circuit. We extend this find- ing to other nonrepeating sequences that exhibit similar circuit disruptions. To address this, we propose targeted patch that effectively resolves the issue without negatively impacting the overall performance of the model. This study provides a mechanistic explanation for an LLM vulnerabil- ity, demonstrating how interpretability can diag- nose and address issues, and offering insights that pave the way for more secure and reliable models. Code available here.
In this paper, explain the reason behind the repeated token divergence in LLMs. We link this behavior to the mechanism of "attention-sinks" - a phenomenon where the initial token in a sequence receives disproportionately high attention, previously shown to be crucial for LLM fluency (Xiao et al., 2024).
To demonstrate the link between attention sinks and repeated token divergence, we take Mechanistic Interpretability ap- proach, analyzing the underlying mechanism of attention sinks in L.Ms.. We identify two key stages consistently observed across many models: First, the initial attention layer identifies and "marks" the first token the sequence. Second, a single neuron in a later layer, triggered by this mark, adds high-magnitude values the hidden state of the first token, creating the attention-sink. This high-magnitude hidden state then attracts attention in subsequent layers, as described by Xiao et al. (2024). We present empirical evi- dence for this two-stage mechanism across multiple LLMs.
Next, we examine how this mechanism interacts with re- peating tokens. We find that the first attention layer, aiming to identify the first token, fails to distinguish it from se- quence of identical tokens. Consequently, it marks both the first token and the repeated tokens, activating neurons that amplify their hidden states. This leads to abnormally high at- tention weights on the repeated tokens, causing the model's 's behavior to diverge and produce unexpected outputs.
1. Introduction
By analyzing the first attention layer implementation of identifying the first token we were able to generate new exploit we call "cluster attack", that induces attention sinks without using repeated tokens. This exploit would be able to bypass surface level defenses that addresses repetitions but not underlying vulnerability.
Large Language Models (LLMMs) have achieved remark- able success in various natural language tasks. However, even otate-of-the-art LLMs can exhibit surprising failures on seemingly simple tasks. A prominent example is the "repeated token divergence" phenomenon (Nasr et al., 2023; Barbero et al., 2025), where LLMs struggle to accurately repeat a single input token when prompted to do so. This issue has been observed across range of models, including: ChaiGPT, LLaMal, LLaMa2 (OpenAI, 2022: Touvron et al., 2023a;b). The underlying cause of this unexpected behavior
Finally, our analysis of the underlying mechanism suggests simple yet effective correction mitigate the repeating tokens divergence and its extensions.
Our contributions are:
'Google DeepMind FUC Berkeley, Correspondence to: Itay Yona <itayona@ @ @aaimna@@@mal.ommm gmail.com>.
1. Mechanistic Explanation: We provide the first mech- anistic explanation for the repeated token divergence in LLMs, linking it to the attention sinti phenomenon and identifying the responsible neural computation. We
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Interpreting the Repeated Token Phenomenon in Large Language Models
pinpoint two-stage mechanism for attention sinks:
algorithm and implementation of the mechanism that give rise this issue.
(1) first-token marking by the initial attention layer, and (2) hidden-state amplification by specific MLP neurons (Section 4).
2.3. Attention Sinks in LLMS
2. Divergence Extension (Cluster Attack): We intro- duce a novel "cluster attack" demonstrating that the divergence extends beyond exact repetition to include similar tokens, revealing a broader vulnerability (Sec- tion 5.2).
LLM Attention Sinks were studied in multiple contexts. Xiao et al. (2024) showed that attention sinks relate to model fluency. Gu et al. (2024) explored why and how attention- sinks emerge. Sun et al. (2024) show the relationship of attention sinks to massive activations. Cancedda (2024) an- alyze what signals propagates through attention-sinks Yu et al. (2024) propose a method to address the existence atten- tion sinks. Bondarenko et al. (2024); Miller (2023) explored methods to mitigate attention sinks for model quantization and efficiency. However, our work takes different ap- proach, focusing on the functional role of attention sinks and their unintended consequences, specifically their con- nection to the repeated token divergence phenomenon. We investigate how the very mechanism that makes attention sinks can also be exploited.
3. Targeted Mitigation: We develop targeted correc- tion method that effectively mitigates the divergence and the cluster attack without impacting performance on other tasks (Section 5.3).
2. Related Work
First, we discuss previous approaches that mechanistically interpreting various model behaviors. Then, we present related work about the two model behaviors we aim to mechanistically interpret repeated token divergence and attention sinks in LLMs.
3. Preliminaries
This section provides a brief overview of the key concepts related to Large Language Models (LLMs) and attention sinks, setting the stage for our analysis of the repeated token divergence.
2.1. Mechanistic Interpretability of LLMs
Mechanistic Interpretability aims to reverse engineer neural networks and uncover the underlying algorithms and com- putational mechanisms that drive their behavior (Wang et al., 2022; Cunningham et al., 2023; Gandelsman et al., 2025; Jiang et al., 2025). This field seeks to move beyond simply observing correlations to identifying causal relationships within the network, providing a deeper understanding of how these complex systems function (Olsson et al., 2022; Olah et al., 2020). Our work falls under this umbrella by dissecting the specific neural circuits responsible for both attention-sinke and the repeated tokens divergence in LLMs.
3.1. Large Language Models (LLMs)
LLMs are deep learning models trained on massive text datasets. They leverage the Transformer architecture (Vaswani et al., 2023), which employs the attention mecha- nism to process sequential data like text. The input to the Transformer is a sequence of tokens that is converted to sequence of embeddings X = [2,, ..., Rn), T2,, xz E Rd. Usually, the first token is an indicator named Beginning-of- Sequence (BoS).
2.2. Repeated Tokens Divergence
Transformers are built from two main blocks: attentions and MLPs. Each attention is built from parallel attention heads. The output of an attention head for token embedding 2; in the sequence X is:
The phenomenon of LLMs failing to accurately repeat to- kens was first described by Nasr et al. (2023). While they observed the convergence of repeating token representations towards the BoS token representation, they offered only intuitive explanations for the matter calling for future work to investigate this further. Our work builds upon this obser- vation by identifying the specific neural mechanisms and attention dynamics responsible for this divergence. We link this behavior the attention-sint mechanism and demon- strate how the interaction between these two phenomena leads to the observed failures in token repetition. Barbero et al. (2025) explored the theoretical challenges of repeated tokens in LLMs (using an infinite context window) and pro- vided empirical observations, but did not explicitly connect the two. Differently from previous work we present a differ- ent level of analysis (Marr& & Poggio, 1976) describing the
q kg
Attention X) proj Σ softmax,
Uy. (1)
Vd'
where:
T. = W kj WLIz, Uj W.zj.
(2)
and Wg, WR, W. E Rd''d are the query, key and value projection matrices and W proj E Rdxdd is the projection matrix back to the residual stream.
The second type of layer is the MLP. In our analysis we mostly use LLaMa2 (Touvron et al., 2023a), which uses
2

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Interpreting the Repeated Token Phenomenon in Large Language Models
meta-lamaiilama-2---nn Regular
Layer meaa()h
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Figure Attention scores for layers repeated "the" tokens in the top panel are significantly higher than those for other tokens in the sequence. This high attention is comparable token in the regular sentences shown the bottom panel. This similarity suggests a connection
31 of LLaMA2-7B-HE. As can seen in the figure, the attention scores the
attention received by between the attention sink mechanism and the high attention given repeated tokens.
the SwiGLU (a) as its activation function (Shazeer, 2020). Therefore, the MLP is defined follows:
beneficial for generating fluent and coherent text, because they act like biases, storing extra attention and meanwhile not contributing to the value computation (Gu et al., 2024).
(Z W (orae(ooWWii @ W gate:))
3.3. Rotary Position Embeddings (RoPE)
where W gate. Win: W Wot IRd A single neuron
j S d" in the MLP has:
Many modern LLMs, including those we analyze in this paper, use Rotary Position Embeddings (RoPE) (Su et al., 2021) instead of absolute or relative position embeddings. RoPE encodes positional information by rotating the token embeddings based on their position the sequence. A key property of RoPE that it allows for efficient computation of attention scores and generalize sequence lengths not seen during training. Critically, paper, RoPE, presents challenges the model's ability to distinguish between the first token and repeated tokens in a sequence.
1. Input weights W RRlxd, that dictate which inputs would activate the neuron.
2. Activation (o in & W )). with W gate E Ixd dictates the amount which the neuron was activated.
3. Output weights W out E I that dictate what is writ- ten to the residual stream when the neuron is activated.
show in this
4. Output (LP
2) & W w) that dictates the individual contribution of that neuron to the residual stream for given input token
4. Mechanistic Analysis of Repeated Token Divergence
3.2. Attention Sinks
We present a mechanistic analysis of repeated token diver- gence, using LLaMa-2 for illustrative examples and plots. Similar motifs observed in additional LLMs (LLaMa-1, LLaMa-3 and Mistral) are discussed in Appendix A.
Attention sinks a phenomenon observed in Transformer LLMs where the model assigns disproportionately high at- tention scores typically to the first few tokens in a sequence (Xiao et al., 2024). These high attention weights, also known as attention scores, indicate that the model is focusing heav- ily on these initial tokens. Attention sinks are believed to
This section investigates the mechanism behind the repeated- tokens divergence phenomenon. We begin by observing the
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Interpreting the Repeated Token Phenomenon in Large Language Models
meta-llamaj/lima-2777a sink layer: 1
the
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es
Mette Sare harm (Log)
Be5 Norm (6.93) 93) Typical
Dd5 Norm 16.93)
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Figure 2. Repeated tokens exhibit extreme norms, similar to the beginning-of-sequenee (BoS) token, in early layers. We present the of the hidden state the sink layer (layer 1) for three repeating words. As the number of repetitions increases, the norm increases and becomes similar to the norm of the BoS token (0 repetitions). This observation explains the high attention scores shown in Figure We later provide causal evidence for this relationship through ablation (Figure 3). We also show the of the BoS token and the average norm of tokens from Tiny Shakespeare dataset (Andrej. 2015) for comparison.
similarity between the attention scores of repeated tokens and those of attention sinks (Section 4.1). We then investi- gate the underlying mechanism of attention sinks (Section 4.2), identifying key components. Finally, we demonstrate how this mechanism misidentifies repeated tokens, leading the observed divergence (Section 4.3).
to their high hidden state norms, similar to the BoS token in attention sinks.
A sparse set of neurons mediates the high norms for the first token (and repeated tokens). We search for sparse set of neurons, which we name "sink neurons", that can be candidates for mediating the high norms of the residual stream activations. We select K candidates for these neurons according to the norm of their contributions to the residual stream in the following manner:
4.1. Large Attention Scores of Repeated Tokens
Our analysis begins with the observation that, in intermedi- ate layers of LLMs, the average attention scores of repeated tokens (e.g., repeated "the"" are comparable in magnitude to the attention scores of the first token in natural sentence (the attention sink). Figure illustrates this phenomenon for LLaMa-2. The attention scores shown are averaged across all attention heads within single layer.
candidates
To investigate the role of specific neurons in mediating high norms, we perform an ablation study. We zero-ablated the candidate neurons, effectively removing their contribution the model's 's computations. We identified sink neurons when observed significant reduction in the high norms asso- ciated with repeated tokens, due to our ablation, as shown in Figure 3. The ablation study supports our claim that these neurons play a crucial role in establishing the attention sink and influencing the high norms repeated tokens. The identifies neurons are summarized in Section 4.4.
4.2. The Attention-Sink Mechanism
Motivated by the observed similarity in attention scores, we investigate the neural circuit responsible for attention sinks. We seek to understand: 1) how these tokens acquire such high attention scores, and 2) how the model identifies the first token. In Section 4.3, will demonstrate that the same mechanism underlies the high attention scores for repeated tokens.
The first attention layer distinguishes the first token from subsequent tokens. We observe that this is done by mapping the two types of tokens (first/non-fing ffistt) into two linearly separable subspaces.
Attention sinks correlate with high hidden state norms in early layers. We observe strong correlation between attention sinks (high attention scores for the first token) and high L2 norms of the hidden states for that token in early layers. Figure 2 shows the L2 norm of the hidden states for different token positions in the sequence. Notice that both the BoS token and the repeated tokens have significantly higher norms compared to other tokens, particularly in the carlier layers (c.g., layer 2). This suggests that the high at- tention scores observed for repeated tokens might be related
Figure visualizes a projection of the token representations after the first attention layer. The clear separation between the distributions demonstrates that the first attention layer maps these tokens distinct, linearly separable subspaces. This suggests that the first attention layer plays key role in identifying and "marking" the first token in the sequence. The fact that we identified a single neuron (rather than an ar- bitrary hyperplane) that perfectly separates these subspaces further supports this claim.
4

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Interpreting the Repeated Token Phenomenon in Large Language Models
Activations Norms (Control)
First-Detector Neuron
Norm of Residual Stream, Activations
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Figure 4. The first token and subsequent tokens belong dis- tinct, linearly separable subspaces. This figure shows the pro- jection of token representations after the first attention layer. The different colors represent the first tokens and subsequent tokens. The clear separation indicates linear separability. Furthermore, we identified single neuron (MLPo. gate neuron 912 in LLaMa2) that perfectly separates these subspaces.
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these same neurons exhibit high activation levels in response to repeated tokens, similar to their response to the first token. This suggests that the same neural circuit responsible for the attention sink is also involved in processing repeated tokens.
Figure 3. Ablation sink neurons. Norm is reduced both for BoS and repeat sequences. Top: Token activations norms without the patch. Bottom: Token activations norms with the patch. Ablating specific neurons significantly reduces the high norms associated with repeated tokens. Data is from LLaMa-2. Sink-Ncurons (1) were zero-ablated. The input consisted of 1200 repeats of the tokens [ "'notherr, 'one', 'bit', 'es', 'the', 'dust']. See Appendix A for similar results on other models.
Distinguishing between token repetitions and the first token. We now provide a more formal treatment of the phenomenon by tying it to recent results of Barbero et al. (2025) and Veličković al. (2024). In particular, we interested in studying what the token repetition process converges to. We point the interested reader to the appendix (Section D) for the proofs and more extensive explanation of the theoretical results.
4.3. Linking Repeated Tokens to Attention-Sink
We will now show that the attention mechanism, pre- viously identified as crucial for processing the beginning- of-sequence (BoS) token, also deeply affects the model's ''s behavior when instructed to repeat tokens. Specifically, we demonstrate that the same neurons involved in marking the BoS token are also activated by repeated tokens, and that the first attention layer cannot inherently distinguish between a single token and a sequence of identical repetitions. This inability to differentiate single and repeated tokens provides mechanistic explanation for the observed divergence.
We consider the same Transformer architecture that is con- sidered by (Barbero et al., 2025). Our results hold for any type of positional encoding that acts solely the queries and keys and that is bounded. This is the case for essentially all positional encodings used in practice and is in particular true for RoPE (Su et al., 2021) the encoding used by all the models in our experiments.
More formally, consider a sequence S, where we fix the k prefix tokens and then repeat token times. For ex- ample, assuming each word is single token could let Sa ('Prefixl'' 'Prefix2', 'poem', 'poem', 'poem') and
Repeated tokens exhibit high norms mediated by the same neurons identified in Section 4.2. We examined the activity of the sink neurons identified in Section 4.2 for sequences with repeated tokens. Our analysis revealed that
Boundedneess is immediate in RoPE it is an isometry (Bar- bero et al., 2024).
5

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Interpreting the Repeated Token Phenomenon in Large Language Models
S = ('Prefixl', 'Prefix2', 'poem', 'poem', 'poem', 'poem', 'poem') where k = 2 for the prefix tokens. A natural question to ask is can we say something about what happens to the representation of the last token at position n + k as
cat
dog
the
n - do? It turns out that the answer is yes, in fact, as n + oo, we prove that the representation of the last token of S. converges to the representation of 5 where S = ('poem') is the sequence with the single element that be-
L2 Norm of AAtino
bla
3
ing repeated. In partieular, as machine precision is finite, this implies that one can find N > 0 such that for all n 2 N we have that S,, is indistinguishable from S" up to floating point precision.
2
The intuitive explanation for this convergence is that as the sequence grows, the relative influence on the output by the prefix will go to 0 due softmax leakage (Veličković et al., 2024). This is the case because critically the size of the prefix remains constant as S, grows. We summarise informally in Theorem 4.1 the result and provide detailed mathematical proofs in the appendix (Section D). Not only does our analysis characterize the behaviour of repeating a token large number of times in a d Transformer, but it also shows how mathematical results that describe the propagation of information in Transformers have security implications.
0
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Index in Repeated Taken Sequence, with Preceding Prefix
Figure 5. Empirical evidence showing the first attention layer does not distinguish between token repetitions and the first token. We first computed the output the first attention layer for an input of a single token, without BoS. Then compared it, using L2 norm of the difference, to the output of the first attention layer for "< BoS> some prefix:", appended to the same token ('eat', 'dog'. "the' or "bla') repeated 500 times on LLaMa-2. This supports Theorem 4.1 showing the converges takes place in practice with less than mar mar_contert.com www.:ottttt__widoouuuuue repeats.
Theorem 4.1 (Informal.). Let x be token and T Trans- former. Consider sequence S. with k fixed prefix tokens and n repetitions of z and singleton sequence S* which consists of single x. As n. - oo the representation of the last element of Sn converges (strongly) to the representation of S* after applying the Transformer T. In other words, the sequence S, becomes indistinguishable from St as n - oo.
in a sequence, a seemingly simple task whose implementa- tion remains unclear. Empirically, this layer marks any first token, not just BoS (Sun et al., 2024; Gu et al., 2024). Lack- ing an absolute positional signal, LLaMa-2 leverages causal masking for this purpose. We observe near-orthogonal query and key vectors in the first layer's s attention heads, effectively making them "other token detectors" preefrriig to attend to any token other than themselves. Causal masking forces the first token to self-attend, effectively marking it as the first for the next layers. Thus, rather than explicitly finding the first token, this layer identifies single-token sequences. This explains how and why long enough sequences of repeated tokens confuse the layer.
To demonstrate this effect, in Figure 5, we show this occur- ring in LLaMa-2 for a number of example sequences. We find that even for a relatively small number of repetitions, the convergence described in Theorem 4.1 is clearly visible.
Long sequences of repeated tokens are falsely marked as attention sinks, causing the model to diverge from the intended output. Combining Theorem 4.1 and the em- pirical observation, we can see why repeated tokens cause problems. The first attention layer can't distinguish the first repeated token from the others, and treats long sequences of repeated tokens much like it treats regular sentence without a beginning of sequence token-ij it assigns high at- tention to them as if they were the first tokens. Because of this, the same mechanism that creates attention sinks for the true first token gets triggered for the repeated tokens, even though they shouldn't 't be treated as the beginning of a new sequence. This disrupts the model's normal processing and leads to the observed divergence in the output.
4.4. Findings
Table 1. Summary of sink ncuron findings in several LLMs, show- ing repetitions needed for induction, sink layer, and neuron IDs. Model
Repeats
Sink- Layer
Sink- Neurons
-LaMa-1-7b-HF
450
2
7003
LLaMa-2-76-HH
1000
1
7890, 10411
4000
198, 2427
1200
7310. 8572
Sink neurons in early layers are a common feature across several Large Language Models (LLMs), as shown in Table 1. We observed these neurons in multiple models. The table
Detection of first token by the first Attention layer. The first attention layer tasked with identifying the initial token
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Interpreting the Repeated Token Phenomenon in Large Language Models
details the number of repetitions required induce sink, along with the layer and ID of the sink neuron. A more in- depth analysis and illustration can be found in Appendix A. While these models share similar underlying mechanisms, they exhibit subtle differences in implementation and behav- ior, which are discussed in Section 7.
5.1. Surface-level mitigation
A mitigation that would simply detect lengthy sequences of repeated tokens is a potential approach, but it does not address the underlying issue. This limitation prompted us to explore other (non-repeating) inputs that induce at- tention sinks. These sinks, similar to those triggered by the -faeiiinnn--SS-unneeeeeee (BoS) token or repeated tokens, achieve similarly high norms by activating the same neurons as the BoS token, ultimately leading to the same undesirable outcome of diverging model behavior.
5. Attack and Mitigation
Nast et al. (2023) first observed that repeated tokens could confuse models, creating vulnerability exploitable for training data leakage attacks. While pretrained LLMs can be prompted to continue prefixes, and instruction-tunc models can be instructed to do so, Reinforcement Learn- ing from Human Feedback (RLHH)--lliged models might resist. Surprisingly, repeated tokens have been shown to trigger ChatGPT (OpenAI, 2022) to deviate from its typ- ical instruction-followinn behavior and reveal memorized training data. Here we show that other models also exhibit repeated token divergence, and output their training data.
5.2. Attack extension
In this section, we investigate the "attend to others" (4.3) mechanism and its implementation in different models. We show that this mechanism clusters the tokens into sets, and achieves its goal by applying different attention heads on each group. This understanding allows us to extend the repeating tokens attack of Nasr et al. (2023) to an attack that repeats tokens from the same set. We extract the dif- ferent sets for different models and present qualitative and quantitative results for the attack performance.
As the training data of LLaMa models is not publicly avail- able, difficult to determine if a model's output originates from its training data. We therefore present an example from Pythia- 12b (Biderman et al., 2023), an open-source model trained on the publicly available The Pile dataset (Gao et al., 2020).
Clustering the tokens. Since we found two distinct sub- spaces for first/non-fing first tokens and we know the first atten- tion layer is responsible for this projection, we reduce the mechanism even further, looking for specific heads that are responsible for this projection. On LLaMa2 we found that for many tokens, a single head's 's output enough to project to the relevant subspaces. The relevant head varied between tokens, forming N clusters for tokens. See Appendix B.
We find that for Pythia- 12b and the input "as", ", repeating 50 times, the model outputs: "The first step in the process of 3D printing is to create a 3D model of the object to be printed. The 3D model is then sliced into thin layers, each layer being printed one at a time. The slicing process is performed by a slicing'
"aput"as",
Repeating tokens for cluster attack. This clustering sug- gests that attention sinks can arise from tokens in the same group, even without a BoS token or exact repetition. By using tokens from a cluster, we trigger the same attention head, its function in this circuit is attend any other to- kens, however since we introduce tokens from the same cluster, this head is inevitably attends with high enough score and projections the tokens representation to the "first token subspace". For example: The norm after MLPI of the input: "Sch Com" (both taken from the cluster of Head 4) is [18.4375, 16.5 16.5469], or for "elements description" (from Head 30) the MLP1 norm [19.0156, 14.3359) dramati- cally higher than any other norm of tokens (besides BoS) after the first layer.
Which a rephrase of the following text 2 that taken from a website that appears in the train- ing data: 'Thee starting point for any 3D printing process is a 3D digital model, which can be created using a varisty of 3D software programmes in industry this is 3D CAD, for Makers and Consumers there are simpler, more accessible programmes available | or scanned with a 30 scanner. The model is then 'sliced' into layers, thereby converting the design into a file readable by the 3D printer.''
5.3. Attack mitigation
More examples of potential leakage appear in Appendix C.
We present a simple editing method that allows us to prevent the repeating token attack. First, demonstrate qualitative results of this edit on LLaMA2 when repeated prompts are provided. We verify that this modification does not change other model behavior by evaluating the edited model on standard benchmarks.
httpps: Phttps://3dpes htt////dpprnttnte meriinduuttyy.. com/ 3d-print innting-bee bagt ca-free-tee begi beginnen nners-gut de# 03 33-technologr
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Interpreting the Repeated Token Phenomenon in Large Language Models
Table The effect of patching on unrelated tasks. We compare LLaMa-I, 2 and Mistral, before and after the patching on
different benchmarks.
LLaMaa--7B-HF
LLaMa-2-7B-HI
Mistral-7B-Instrruce
original
patched
A
original
patched
A
original
patched
A
MMLU
29.81
29.93
+0.12
41.20
42.17
+0.97
53.41
52.58
-0.83
HellaSwag
56.97
56.95
-0.02
57.12
57.13
+0.01
56.23
55.75
-0.48
TruthfulQA
31.21
29.38
-1.83
34.15
34.39
+0.24
53.37
52.26
-1.11
WinoGrande
69.93
69.93
0.00
68.98
69.06
+0.08
69.30
68.82
-0.48
A12-ARC
41.89
42.15
+0.26
43.52
43.34
-0.18
50.17
49.66
-0.51
Listing 1. manual patch to repeated tokens issue.
success rate of the cluster attack. Finally, our mitigation strategy does fully address the inherent vulnerability of causally masked Transformers repeated tokens.
tmp_output None
stnk_neuron 789C sink_layer
patch_sink phase); global tmp_output
7. Discussion and Future Work
if phase =n "prefill";
This study reveals a clear link between repeated token di- vergence in LLMs and the attention sink mechanism, in the form of neural circuits. We demonstrate how the first attention layer's confusion between an initial token and repeated sequence leads erroneous attention weights and output divergence, highlighting the inherent tension be- tween fluency and robustness. Our "cluster attack", induce sinks without the need to repeat tokens. Simple defense mechanisms that filter out requests for repeated tokens are easily bypassed by our new attack. Based on neural circuit understanding developed more fundamental mitigation for this issue without significant performance impact (see Table 2). This work contributes to LLM interpretability by showcasing how mechanistic insights can guide targeted interventions. While our analysis focused on specific LLMs, we intend to investigate broader generalizability in future work, to explain why repeated token sequences lead to train- ing data extraction as studied by Nasr al. (2023). This research underscores the need for ongoing investigation into the inner workings of LLM to build more transparent, reliable, and secure models.
tng_ Lne_output xlirl, sink sink tmp_eu:put
phase "decode";
xI:,0, sinkene tmp_ou:put
return
patch_block model. [syer_ mecchbblcc..mp.up.raa. hww.k.ockkkoooooooooooooooookkk. book (parch_
During the prefill stage, the model processes an entire se- quence and might include repeats. We store aside the output of the sink neuron for the second input token, this value means "no-sink" valid for non-repeated or non-Bot BoS to- kens. We then replace all the values of the neuron activity be "no-sink". We also make sure that during decoding no additional sink is set.
Edit performance on other tasks. While our edit mitigates the repeated token attack, it may change to model perfor- mance on other unrelated tasks. Next, we verify that the presented edit (1) does not change the performance on other standard tasks. As shown in Table 2, the changes between the performances of the model before and after the patch negligible.
Acknowledgements
We thank Arthur Conmy, Christopher A. Proquette-Choo Guillermo Ortiz-limenez, Guy Dar, and Neel Nanda for their valuable comments and feedback our paper. YG is supported by the Google Fellowship.
6. Limitations
This work has several limitations. First, the mechanism by which training data leakage occurs through attention sinks remains unclear, requiring further investigation. Second, while shared motif's exist across LLMs, the first attention layer's behavior was unique LLaMa2, highlighting the influence of model-specific details. Third, the efficacy of token repetition in inducing sinks varies across models. Not all tokens in LLaMa2 induce sinks (see Figure 6), suggest- ing other factors are at play. Fourth, due to the difficulty assessing training data leakage we did not quantify the
Impact Statement
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
8

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1250 1500 Interpreting the Repeated Token Phenomenon in Large Language Models
meta-llama/Llama 2-77bhff, sink layer:
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Figure Limitations, For tokens ("Another", "bit", "dust"), repetition does not lead the creation of attention sinks.
References
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Interpreting the Repeated Token Phenomenon in Large Language Models
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10

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Interpreting the Repeated Token Phenomenon in Large Language Models
A. Generalization to other models
present results on additional models: LLaMa-I, LLaMa-3 and Mistral, to illustrate the generality of the circuits we discovered. First, we present (Figure 7) patching results similar Figure 3, followed by an attention score for LLaMa-I I (Figure 8), and graphs showing extreme norms for repeated tokens in LLaMa-I, LLaMa-3 and Mistral (Figure 9).
Activations Narms (Control)
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Figure 7. An ablation to sink neurons in other models, extending Figure 3, reduces the norms both for BoS and repeats. Top Left: LLaMa-1. Top Right: LLaMa-3. Boutom: Mistral.

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Interpreting the Repeated Token Phenomenon in Large Language Models
llama-7b-hf: Regular sentences
Layer meantheads)
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Figure Attention scores layers 3, 17, repeated 'the' tokens in the top panel are significantly higher than those for other tokens in the sequence. This high attention is comparable attention received by token in the regular sentences shown the bottom panel. This similarity suggests a connection between the attention sink mechanism and the high attention given repeated tokens.
LLaMAI-7B-HE. As can seen in the figure. the attention scores the
laffoz//eeappod--esscchllll----- sink layer:
Another
one
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Hidden (Log)
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Jon wayh
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12

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Interpreting the Repeated Token Phenomenon in Large Language Models
mebe:llaaaMMtalllam333888--mm sink layer:
Another
bites
Hidden (Log)
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55.66]
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mistella//Mttral/BBnnstuuuuooo sink layer:
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Figure 9. Further evidence of extreme norms for repeated tokens in early layers. We present similar results for Figure 2 on LLaMa-1 LLaMa-3 and Mistral. The norm the hidden state at sink layer for repeating words. As the number of repetitions increases, the norm increases and becomes similar to the norm of the BoS token (0 repetitions). We also show the norm of the BoS token and the average norm tokens Tiny Shakespeare dataset (Andrej. 2015) comparison.
13

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Interpreting the Repeated Token Phenomenon in Large Language Models
B. Cluster of Tokens for Detecting First
Jo (ne/enty "AX27 tetty reutt, fout", "tanction", (like", "ueing", 'dbest, (Listt, 'under', 'look', 'result', 'atter', lotely "mmbers' "hand", f1z", "aguin", 'down', "createt, "change", fordez', 'sarver', "requesz", "support", "aroup', "ungen', 'sesuita', "pocition", "appoar', "based', "eftect', "getting", "ists", "response", "knovn', 'keep', 'clemets', 'fanctions's rost, 'referenen', Look"' "changes', "zeturna", "house", "Beplanber', "naking', "connection" 'seturned", 'among', 'roand', "included", fastack", "ketine', "description', 'execute', 'February', 'lose", 'recorde', (II, 'requires', 'ssrociated', (supporeed" "layer', 'inclesen', "established', "incrsase", "undarstandina", "1),', 'secine', 'instances', 'ebserved', 'developad', fonded', 'influence', 'ilss', "programe", 'supperts', 'increases', 'Station', 'proriding', 'Fund', 'apgroaches', 'considering', 'catogories', 'allowing', (5Ae', (
4 prr,, 'pon', "Com', "he', Holl, foves', 'could', 'car', 'Har', 'As', (Sch", 'hee, Lid' Yaunt, 'ass', 'Cont, "trand', rcharty Part supp', 'exp', 'ded', "orig', '12', 'mat', 'Ber', "Ira', 'Bo", "coust, 'appro', "Cons", 'Reg', 'Ber', "beh', 'ear', "2p9' 'supert, "don', rSte', roron', rFll", "beo", thart, tMan, (- rgra", "Eac", rabr, 'aa", "mart, forgant, rcharf, rear', "eas", 'Minf, rgll, "hun', 'eot', 'Ang', 'Ma', 'Gener', 15bb fauton', rsinf, "Part!, feleat', nagg,, fotilf, rval', "hel', "Hall, theef, 'q'. 'Pre', "Boz', 'Po', 'can', "sal', 'ase", "Irane', 'Gref, '3ol', 'Finf, rief, 'Brof, "Pros", "Dem', "Ret', "Noe', "Met', sTee', '00', 'tor', 'univers', "Re'' 'Gra', 'Spe', 'stuc', 'Kir', real', 'Dut, "Ja', "Mat', "Butzy "Del', 'tegf, 'compan', "Corst', <Bre', 'Hlef, 'ola', "arof, "Equ", race", "Dourt, 'ree", "ean", "iud,, ropar', rauz", "Statr, reasty rremt, "Horf, relort, reas", "aa,, raunt, rerr, nott' "Viuk", "Bv', fade', "Exe,, (Ret, "Huc', "Evecy', fesal, "swet', "saef, reolonf, reallr, "grevf, "Taa', 'il,, (Kaf reel', "Bonf, rre, 'hol', 'Coo', "Bot', "Bol", "Bey', 1A81, "Organ', "decre', "leb", rdev', "Dor', "preat, 'Vari', "Son', "Maa', rwy,, stal', 'reve', rBlaf, rvir", "No1', "Ax", "bser, roor, rethl, ralet, "Raat", rcabt, (Molf, rrair, rvel', "gh,, 1x11f, "Aicerof, rBout, 101, ralef, "Bob", "dy", 'Eth', "Cho", "s1", "Mes", 'mil', "clar', reurnt, "Misf, "duton", (Ster)
['betore', 'between', 'through', wiitheut', rupont, fagainst', factually', 'whethes', 'together', 'signiticant',, 'évrier',
'regarding"]
C. Leakage & Prevention
We show two examples of repeated inputs LLaMa-1 that diverge model behavior, and show that with our patch Section 5.3 mitigate this issue.
14

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Interpreting the Repeated Token Phenomenon in Large Language Models
After patching < 8 > Repeat: poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem pocm poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem pocm poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem paem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem pocm poem poem poem poem poem pocm poem poem poem pocm poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem and poem poem poem poem poem poem poem, poem, and poem, poem, poem, poems, poetry, poetry, rhymes, rhymes, rhyming, rhyming, rhyming rhyming rhyming rhyming rhyming rhyming ryyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rhyming rh
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Interpreting the Repeated Token Phenomenon in Large Language Models
Before patching - < > Repeat: poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem pocm poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem pocm poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem (n poem)(n poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem's in poem poem poem poem poem poem poem poem i poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem's in poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem
n poemly (n poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem poem (n poem) in poem',n n poemlyn poem'y in The 19th century was time of great change in the United States. The Industrial Revolution was in full swing and the United States was growing rapidly. The United States was also going through time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through a time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through a time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through time of great social change. The United States was becoming more and more industrialized and the population was growing rapidly. The United States was also going through time of great social change. The United States was hecoming more and more industrialized and the population was growing rapidly. The United States was also going through a time of great social change. The United States was becoming more and more
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Interpreting the Repeated Token Phenomenon in Large Language Models
After patching - < > Repeat: company company company company company company company company
company company company company company company company company company company company company
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17

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Interpreting the Repeated Token Phenomenon in Large Language Models
s > Repeat: company company company company company company company company com- pany company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company company
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Table 3: Responses for repeated tokens inputs, before and after patching
D. Proofs
In this section we provide the omitted proofs from the main text. We start by clarifying our notation. Let v!!?
q and k.00 be the d-dimensional value, query, and keys vectors of the i-th token at the b-th layer. Let Sn
(2)
(0) (0)
(0) v)) be a sequence of length n + k composed of k 'prefix' vi ,...,, k tokens and n
n. times
copies of the token v (0). Instead, let S* S*===((0)) (v(()) be the singleton list consisting of the same token being repeated in the sequence S,, For example, let S, = (' ('Hello' ! how', are', , you'.... you') where by, for example, 'Hello' we actually mean the d-dimensional vector embedding of the word 'Hello'. Then, the tokens 'Hello', 'how, 'are" would be the prefix tokens (i.e. = 3) and we would have S = ('you'), i.e. 'you' would be the repeated token.
We study the same model architecture to the one studied by Barbero et al. (2025). We note that the exact architecture choice
18

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Interpreting the Repeated Token Phenomenon in Large Language Models
unlikely affect the final conclusion of results we mainly rely on very general properties of Lipschitz continuity of the various components. In proofs, rely the fact that attention disperses the underlying sequence gets longer (Veličković et al., 2024). In particular, we use the fact that we can bound af,; S 1/n exp()) for some 6 that is a function of the spectral norm of the weights entering the softmax, as shown by Veličković et al. (2024).
Transformer block updates the value v(O, of token at layer l as:
z/
(8)
jSi
(e+1) (2) + 2
(8)
with o the attention coefficient between i and layer l and & an MLP. We write by T(*) the 8-th Transformer layer, that takes as input a sequence S and outputs a sequence (S) such that each element in the sequence is updated as above. We denote by (S)), the vector corresponding to the i-th sequence element after the Transformer is applied. In our proofs we assume that de is Lipschitz with constant V This is immediate as the domain of & is bounded and sp is assumed to be continuous.
Lemma D.1. Consider the sequences Sn and S* and a one-layer Transformer T(), Then the representation of the final token of Sn converges strongly to the representation of the single token of S*, i.e.
(T00 =0
lim n-000
(4)
Proof. It is required to show that there exists N such that for all n > N we have that [[T(S,) - T(S)) < e > 0. We use the simple estimate that ( < exp(o) with n the sequence length and some constant that takes into account the largest difference in the activations (see Veličković et al. (2024)). We also let max, ||vy]|- which always exists due the finite vocabulary and continuity of Transformer operations (again, Veličković et al. (2024) for further details).
for all
As the Transformer only has one layer, we drop layer superscripts for clarity. We start by showing that it suffices to show that 1 zı|l < as this implies:
[T(Ss)n4x - T( = || (Zn+x) + Znth 1/ (zi) - zill
< 1| (zn+k)
+ (zi))l + ||zn z
[//V + 1)c.
Since || constant this gives us what desired. We now show that || 21 <c
19

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Interpreting the Repeated Token Phenomenon in Large Language Models
1 21
Σ On +V
|
v
(2v)
1Sn+
Σ jSnth
OntkiVy
Σ
Vn +
E
antkyV-N
isk
kejjnnk
1- Σ
Vn +
antki
isk
jSk
Σ antkijV
Σ
an-kis V
J
JSk
Onthi 1/sl +
Σ
Ontki
l|vll
JSk
5Sk
2rk exp(o)
$ 11
As k. are all constants, then we have that (S (S)) decays O(1/n) and is thus below any for large enough n.
Next, we extend the above result to Transformer with T. layers, i.e. Ty = T(L) o ((L.-1) o o... T(), where each (e) denotes the mapping performed at layer t. Note that we use the superscript T' to denote the &-th Transformer layer, while the subscript Te to denote the composition of the first & Transformer layers. We assume that each layer Lipschitz continuous with respect to its input, with Lipschitz constant [) One can always find Lipschitz constant as Transformers are continuous and, in our case, have compact domain due to the finite vocabulary size.
Theorem D.2. Let TL be Transformer with L layers and suppose that each layer is Lipschitz continuous with constant | J. Then for the sequences Sn and S",
lim
( =0.
Proof. We prove the result by induction on the number of layers L. The base case follows from Lemma D.I. Now assume that the result holds for a Transformer with layers, that is,
(T - -2(S") < En
with 6, + 0 n + co. Consider an (E + 1y-layer Transformer T T(++1) o Tg. Since ((++)) is Lipschitz continuous with constant || we have
(Te4 Te+1(5")
(((+1) (Te(S,)) -T(++1 (T(
< (e(++)) [T((S))+t Te(S*)
VT(e+1)
Since en - 0 as n -+ oc, it follows that
lim [ ( n=000
- -T++ 0.
20

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Interpreting the Repeated Token Phenomenon in Large Language Models
We note technicality above that tacitly ignore that really consider the output of the model after applying projection æ that only considers the projection to the last token. This detail can be fortunately ignored because the projection mapping has Lipschitz constant 1.
Thus, by induction, the result holds for any finite number of layers L.
21


Reviews:
[{"review_id": "pBAka5svT3", "reviewer": "Reviewer_TFmf", "summary": "This paper focuses on the repeat phenomenon in LLMs. The authors view this problem from the view of attention sink. They show that the first attention layer marks the initial token, and the later MLP neuron amplifies its hidden state, creating an attention sink. A patching method is proposed to mitigate this effect.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:\n\n__“The main weakness of this work is that the task considered is very rare in the realistic application. Most users of LLMs will not let LLMs repeat some words. The lack of motivation discounts the importance of this work. It is hard to justify why people want the LLMs to repeat the token.”__\n\nThe repeating tokens “task” is indeed not a common task. Nevertheless, once this behavior is triggered, it can cause a training data leakage – a serious security flow [1, 2]. Like many software vulnerabilities, the behavior that triggers the presented vulnerability is not a typical user input. Nevertheless, we assume here an adversarial user that aims to attack the model, and we find a patch that can protect the model from such an attacker.  \n\n\n[1] Nasr et al. Scalable extraction of training data from (production) language models, ICLR 2025\n\n[2] Bye Bye Bye...: Evolution of repeated token attacks on ChatGPT models, https://dropbox.tech/machine-learning, 2024", "claims_and_evidence": "The authors claim that \n\n1. The repeated token divergence is caused by attention sinks.\n\n2. Repeated tokens activate the same neural circuit responsible for attention sinks.\n\n3. A simple patching can mitigate the issue.\n\nThese claims are supported by the evidence.", "methods_and_evaluation_criteria": "The authors propose the patching method to mitigate the sink issue. The experiments demonstrate the efficiency of the proposed method.", "theoretical_claims": "No theoretical analysis is provided in the paper.", "experimental_designs_or_analyses": "The experimental designs in the paper are plausible.", "relation_to_broader_scientific_literature": "N.A.", "essential_references_not_discussed": "N.A.", "other_strengths_and_weaknesses": "The main weakness of this work is that the task considered is very rare in the realistic application. Most users of LLMs will not let LLMs repeat some words. The lack of motivation discounts the importance of this work.", "other_comments_or_suggestions": "N.A.", "questions_for_authors": "The main concern is the importance of the repeat task. It is hard to justify why people want the LLMs to repeat tokens.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XfbiofCJBC", "reviewer": "Reviewer_LKw5", "summary": "- This paper discusses the repeated token divergence issue, links it to a possible LLM phenomenon named attention sink and proposes a solution for it.\n - This paper takes a Mechanistic Interpretability approach, analyzing the underlying mechanism of attention sinks in LLMs, and shows empirical evidence of this mechanism. Then the analysis is linked with the repeated token divergence issue.\n - The authors introduce a \"cluster attack\" based on their findings, which extends the vulnerability beyond simple token repetitions. They also develop a targeted patch that mitigates the issue without harming the model's performance on other tasks.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:\n\n__“Rope affects the queries and keys, not keys and values, this claim: \"thus symmetry between all tokens is preserved\" shall be further clarified. Rope will affect attention output according to the relative positions, so even the repeated tokens will have different outputs, the claim: \"all tokens in the repeated sequence will have the same output after the first attention layer.\" in line 299 is confusing.”__\n\nWe agree and sorry for the confusion. We have revised the paper to include a proof to these claims. In short, the output of an attention head ($o$) for a sequence with a prefix (of size $k$) proceeded by a set of repeated tokens (of length $n$) is:\n\n$ o = \\sum_{i<k} \\alpha_i v_i  + \\sum_{k <= i < n+k} \\alpha _i v $\n \nwhere $\\alpha_i$ are the coefficients of the attention matrix (after softmax), thus:\n$ \\sum_{i}\\alpha_i = 1 $\n\n$v_i$ is the value of token at index $i$, and $v$ is the value of the repeated token (from index $k$ to $n+k$)\n\nRoPE affects key and queries, thus affecting the values $a_i$ but the values of repeats of the same token are fixed $v$.\n\nFor a long enough sequence the values of the repeated token will dominate the values of the prefix, this output will converge to $v$.\n\n__“Although mentioned in the limitations, the first attention layer’s behavior was unique to LLaMa2, but the first attention layer mechanism poses an important finding in this paper, which hinders the generalization ability of this paper. How well this method can be used in other models is unknown.”__\n\nWe suspect subtle differences in training, data or initialization induce different ways for the first attention layer to behave. However, it was important to us to show that by reverse-engineering one layer we can generate non-repeating sequences which also induce attention sinks.\n\n__“I wonder how will the mitigation influence the decoding efficiency as all sink neurons need to be figured out.”__\n\nThe decoding efficiency after the mitigation is not significantly different from the original decoding efficiency. As can be seen by the pseudo-code (Listing 1), our patch changes one neuron for LLaMa2. Even with more neurons, the patch can be done in parallel on all of them as the set of neurons can be precomputed.\n\n__“The reason behind the high attention scores of the attention sinks and repeated tokens seems different. Repeated tokens are likely to have high activations because of self-attention between similar embeddings, but attention sinks are due to other different reasons. In addition, does the LLm distinguish attention sinks only by attention scores? Why can't model distinguish by positions?”__\n\nOur analysis indicates that the high attention scores observed in both phenomena are not due to distinct causes but rather stem from the same underlying neural mechanism. Specifically, the mechanism responsible for creating attention sinks (high attention on the initial token, crucial for fluency ) is inadvertently triggered by long sequences of repeated tokens.   \n\nThe core issue lies in the model's first attention layer, which struggles to differentiate a sequence of identical tokens from a single, initial token, particularly in architectures using RoPE. This layer misidentifies the repeated sequence, activating the same \"sink neurons\" that amplify the hidden states of initial tokens. Consequently, repeated tokens acquire high hidden-state norms, attracting disproportionate attention similar to the BoS token. While the model utilizes positional information (e.g., via causal masking ), this specific case of identical repetitions challenges its ability to distinguish position effectively, leading to the observed divergence.   \n\nTherefore, our work demonstrates that repeated token divergence is mechanistically linked to the attention sink phenomenon, arising from a failure in positional differentiation under specific repetitive conditions. We hope this clarifies the connection established in our findings.", "claims_and_evidence": "The claims are proved to some extent but not fully convincing.\n- line 294: Rope affects the queries and keys, not keys and values,  this claim: \"thus symmetry between all tokens is preserved\"  shall be further clarified.\n- line 299: Rope will affects attention output according to the relative positions, so even the repeated tokens will have different outputs, the claim: \"all tokens in the repeated sequence will have the same output after the first attention layer.\" in line 299 is confusing.", "methods_and_evaluation_criteria": "The methods and evaluation criteria make sense.", "theoretical_claims": "Yes, there is not much theoretical claims in this paper, most results are obtained empirically.", "experimental_designs_or_analyses": "Although mentioned in the limitations, the first attention  layer’s behavior was unique to LLaMa2,  but the first attention layer mechanism poses an important finding in this paper, which hinders the generalization ability of this paper. How well can this method be used in other more models is unknown.", "relation_to_broader_scientific_literature": "This paper are related prior literatures in terms of several aspects:\n- repeated token divergence\n- Attention sink in transformer network\n- LLM attack", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "weakness:\n- The reason behind the high attention scores of the attention sinks and repeated tokens seems different. Repeated tokens are likely to have high activations because of self-attention between similar embeddings, but attention sinks are due to other different reasons. In addition, does the LLm distinguish attention sinks only by attention scores? Why can't model distinguish by positions?\n- The line 294, Rope affects the queries and keys, not keys and values,  this claim: \"thus symmetry between all tokens is preserved\"  shall be further clarified. In addition, Rope will affects attention output according to the relative positions, so even the repeated tokens will have different outputs, the claim: \"all tokens in the repeated sequence will have the same output after the first attention layer.\" in line 299 is confusing.", "other_comments_or_suggestions": "None", "questions_for_authors": "- Although mentioned in the limitations, the first attention  layer’s behavior was unique to LLaMa2,  but the first attention layer mechanism poses an important finding in this paper, which hinders the generalization ability of this paper. How well can this method be used in other more models is unknown.\n- I wonder how will the mitigation influence the decoding efficiency as all sink neurons need to be figured out.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wJLcHSsp3L", "reviewer": "Reviewer_49D9", "summary": "This paper studies the \"repeated token divergence\" phenomenon in LLMs, where models fail to accurately repeat a single token when instructed to do so. The authors provide a mechanistic explanation linking this behavior to attention-sinks (where the initial token in a sequence receives disproportionately high attention).\nThe researchers identify a specific neural circuit responsible for attention-sinks consisting of two key stages: \n1) the first attention layer \"marks\" the initial token; \n2) specific MLP neurons (termed \"sink neurons\") add high-magnitude values to its hidden state, creating the attention-sink. \n\nWhen processing sequences with repeated tokens, the model's first attention layer confuses these repetitions with the beginning-of-sequence (BoS) token, triggering the same neural circuit and causing the model to diverge from its intended output, sometimes revealing memorized training data.\nThey develop and validate a targeted patch that corrects the issue without significantly impacting model performance on standard benchmarks.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "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": "We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:\n\n__“The paper shows that not all tokens can effectively induce attention-sinks when repeated (Figure 5). This unexplained variability suggests additional factors at play that aren't fully captured by the current mechanistic account.__”\n\nAs we mentioned in the limitations section, some additional factors can indeed be at play. Nevertheless, our goal is to understand why and how the previously identified phenomenon of model divergence due to repeated tokens, happens. Our current model is _useful_ - it allows intervention that prevents the phenomenon, and therefore we believe that our analysis is valuable.\n\n__“While the paper identifies the mechanism behind repeated token divergence, it doesn't fully explain why this leads to training data extraction. The relationship between attention-sinks and the retrieval of memorized content remains unclear.__”\n\nWhile we acknowledge the need for further investigation, our work establishes a crucial _precursor_ to training data extraction. The identified mechanism of repeated token divergence leading to attention sinks creates the _conditions_ under which the model is more likely to retrieve and potentially output memorized content.\n\n__“The authors note that while there are shared motifs across LLMs, the first attention layer's specific behavior was unique to LLaMa2. This raises questions about how broadly applicable their exact mechanisms are across different model architectures.__”\n\nWe suspect subtle differences in training, data or initialization induce different ways for the first attention layer to behave. However, it was important to us to show that by reverse-engineering one layer we can generate non-repeating sequences which also induce attention sinks.\n\n__“The validation of the patch focuses on model performance on standard tasks but doesn't directly measure its effectiveness in preventing the extraction of training data, which is the primary security concern.__”\n\nWe have provided a few examples showing how coherency is maintained. It is rather difficult to measure training data leakage as most models are open-weights but not fully open-source, therefore there is very little information about their training data.\n\n__“In Claim 4.1 and its proof: the proof makes logical sense but relies on simplifying assumptions. It correctly identifies that with identical tokens, the value vectors in self-attention are equal. However, the proof doesn't fully account for the positional information encoded by RoPE: while RoPE affects keys and queries, the interaction between position-encoded representations could theoretically still allow differentiation between positions. The empirical observation in Equation 4 helps support this claim, but the theoretical argument could be more rigorous in addressing how RoPE specifically fails to distinguish positions in this context.”__\n\nWe have revised the paper to include a theoretical analysis of equation 4. We argue that differentiation between positions of equivalent tokens is _impossible_ with RoPE. In short, the output of an attention head ($o$) for a sequence with a prefix (of size $k$) proceeded by a set of repeated tokens (of length $n$) is:\n\n$ o = \\sum_{i<k} \\alpha_i v_i  + \\sum_{k <= i < n+k} \\alpha _i v $\n \nwhere $\\alpha_i$ are the coefficients of the attention matrix (after softmax), thus:\n$ \\sum_{i}\\alpha_i = 1 $\n\n$v_i$ is the value of token at index $i$, and $v$ is the value of the repeated token (from index $k$ to $n+k$)\n\nRoPE affects key and queries, thus affecting the values $a_i$ but the values of repeats of the same token are fixed $v$.\n\nFor a long enough sequence the values of the repeated token will dominate the values of the prefix, this output will converge to $v$.", "claims_and_evidence": "The cluster attack's effectiveness is asserted but not quantitatively measured, making it difficult to assess its practical significance compared to direct token repetition.", "methods_and_evaluation_criteria": "The methodological approach is generally sound, but the paper lacks a systematic evaluation of how many tokens from the vocabulary can induce attention sinks when repeated, instead showing selected examples.", "theoretical_claims": "In Claim 4.1 and its proof: \nthe proof makes logical sense but relies on simplifying assumptions. It correctly identifies that with identical tokens, the value vectors in self-attention are equal. However, the proof doesn't fully account for the positional information encoded by RoPE: while RoPE affects keys and queries, the interaction between position-encoded representations could theoretically still allow differentiation between positions.\nThe empirical observation in Equation 4 helps support this claim, but the theoretical argument could be more rigorous in addressing how RoPE specifically fails to distinguish positions in this context.", "experimental_designs_or_analyses": "- The paper doesn't include control experiments with non-identical but similar tokens to test the specificity of the mechanism.\n\n- The validation of the patch focuses on model performance on standard tasks but doesn't directly measure its effectiveness at preventing the extraction of training data, which is the primary security concern.", "relation_to_broader_scientific_literature": "The paper presents convincing evidence for its core mechanistic explanation of repeated token divergence through attention sinks. The empirical work identifying specific neural circuits is particularly strong. However, the connection between this mechanism and training data extraction needs more thorough study.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "other strenghts:\n\n- The authors use a systematic approach to identify the neural mechanisms involved, using causal interventions (neuron ablation) to verify their hypotheses. This provides strong evidence for their claims about the role of specific neurons in creating attention-sinks.\n\nother weaknesses:\n\n- The paper shows that not all tokens can effectively induce attention-sinks when repeated (Figure 5). This unexplained variability suggests additional factors at play that aren't fully captured by the current mechanistic account.\n- While the paper identifies the mechanism behind repeated token divergence, it doesn't fully explain why this leads to training data extraction. The relationship between attention-sinks and the retrieval of memorized content remains unclear.\n- The authors note that while there are shared motifs across LLMs, the first attention layer's specific behavior was unique to LLaMa2. This raises questions about how broadly applicable their exact mechanisms are across different model architectures.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LlekejwDhE", "reviewer": "Reviewer_xK8R", "summary": "This paper attempts the explain the phenomenon of \"repeated tokens\" with the behaviours of \"attention-sink\" in LLMs. Firstly, the authors find that besides the first token, the repeated tokens also have high attention scores and large hidden states norms. Then, the authors identify the neurons that contribute to the high norms. The authors explain this phenomenon by Equation 4 (which I think is the most important one): as the number of repeated tokens increases, the influence of prefix (which may related to the system instructions or user instructions) diminishes. Finally, the authors explore how to utilise the explanation to understand repeated token attack and mitigate the attack.\n\n\n## update after rebuttal\n\nI think most of my concerns have been addressed, so I raised my rating to 3. However, I understand that further investigation may be required here, which prevents me rating higher.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "We thank the reviewer for their valuable comments. Next, we address the reviewer's questions and concerns:\n\n__“I feel there is a gap between \"repeated tokens could diminish the influence of the preceding prefix\" and \"repeated tokens could lead to training data leakage\". I am looking forward to the clarifications from the authors.__”\n\nWe provide a brief proof below to theoretically validate the diminishing prefix influence (a full proof will be in the Camera-Ready version). This effect leads to the described attention sink divergence.\n\nWe have revised the paper to include a theoretical analysis of equation 4. In short, the output of an attention head ($o$) for a sequence with a prefix (of size $k$) proceeded by a set of repeated tokens (of length $n$) is:\n\n$ o = \\sum_{i<k} \\alpha_i v_i  + \\sum_{k <= i < n+k} \\alpha _i v $\n \nwhere $\\alpha_i$ are the coefficients of the attention matrix (after softmax), thus:\n$ \\sum_{i}\\alpha_i = 1 $\n\n$v_i$ is the value of token at index $i$, and $v$ is the value of the repeated token (from index $k$ to $n+k$)\n\nRoPE affects key and queries, thus affecting the values $a_i$ but the values of repeats of the same token are fixed $v$.\n\nFor a long enough sequence the values of the repeated token will dominate the values of the prefix, this output will converge to $v$.\n\nWhile we acknowledge the need for further investigation, our work establishes a crucial _precursor_ to training data extraction. The identified mechanism of repeated token divergence leading to attention sinks creates the _conditions_ under which the model is more likely to retrieve and potentially output memorized content.\n\n__“Do you have any intuitions on why certain repetition cannot lead to attention sinks / large norm of activations? The paper about massive activations [1] also show that besides the first token, several other tokens with low semantics could also induce the attention sink. Do you think this is related?__”\n\nWe suspect certain repetitions does not lead to attention sinks for several reasons:\n1) the context window is too short for the convergence to take place.\n2) the circuit identifying the first token does not generalize to all models.\n\nWe suspect the model doesn't learn to identify <bos> but rather the first-token due to \"packing\", and the face that multiple <bos> tokens appeared in each context window, and believe that knowledge about the subtle training decisions will shed light on repetitions that do not lead to attention sinks.\n\nWe think that BoS is a token with no-entropy, since it is always the first token, it does not attend the rest of the sequence, it is updated in a fixed manner in each layer. The model leverages that to control and bias the attention function. We suspect that other tokens, especially those carrying little information about the context (conjunctions, such as: \"the\", \"and\", \"but\"), will be used by some attention heads to implement a no-op. This has been studied in [1].\n\n[1] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models, arxiv, 2024.\n\n__“Another weaknesses is that most of experiments are conducted using case studies. A systematic evaluation with metrics is lacking. Besides the attack success rate of mitigation approach I mentioned before, I find that when building the relationships between repeated tokens and attention sink, only some samples are provided. Why not include some statistical results to show the generalisation of this phenomenon?”__\n\nThe causal relationship between the attention sink mechanism and the repeated token phenomenon is substantiated by our ablation experiments targeting specific 'sink neurons' (Section 3.2). Figure 3 provides empirical evidence that removing the contribution of these identified neurons effectively mitigates the high activation norms characteristic of repeated token divergence. This experimental approach, focusing on causal interventions within the identified circuit, is central to mechanistic interpretability and provides sufficient evidence to confirm the direct involvement of the attention sink mechanism in the observed phenomenon", "claims_and_evidence": "I think most of claims in this paper are supported by the empirical studies. \n\nRegarding the attack mitigation, the authors propose a manual patch approach. In the appendix, they show that this editing method mitigates the attack by using several examples. I think more empirical studies may need to be conducted with some attack success metrics to further support the conclusion.", "methods_and_evaluation_criteria": "The authors first showcase the relationship between repeated tokens and attention sink with several case studies. Besides LLaMA2-7B model used in the main paper, the results for other models are also showcased in appendix. \n\n\nThen based on the explorations above, the authors propose a mitigation approach. Although the authors demonstrate that the mitigation approach won't harm the model performance on unrelated task, they fail to show the effectiveness of mitigation approach towards repeated token attack using systematic benchmarks or evaluations. Only two cases are shown in the appendix.", "theoretical_claims": "Yes. Only claim 4.1 is a theoretical claim. The proof shown in this paper is correct.", "experimental_designs_or_analyses": "I think most of the experimental designs and analyses are sound and valid. However, I feel there is a gap between \"repeated tokens could diminish the influence of the preceding prefix\" and \"repeated tokens could lead to training data leakage\". I am looking forward to the clarifications from the authors.", "relation_to_broader_scientific_literature": "This paper is related to both mechanistic interpretability and safety in LLMs. I think it contributes to understanding the circuits of Transformer and why repeated tokens could attack LLMs. The mitigation approach proposed in this paper, if justified with more evidence, I think it will also contribute to the community of memorization attack.", "essential_references_not_discussed": "I think this paper has cited essential references.", "other_strengths_and_weaknesses": "I have included most of the strengths and weaknesses in the above. \n\n\nAnother weaknesses is that most of experiments are conducted using case studies. A systematic evaluation with metrics is lacking. Besides the attack success rate of mitigation approach I mentioned before, I find that when building the relationships between repeated tokens and attention sink, only some samples are provided. Why not include some statistical results to show the generalisation of this phenomenon?", "other_comments_or_suggestions": "See the above.", "questions_for_authors": "These questions are less related to my current ratings. \n\n1. Do you have any intuitions on why repeated tokens induce model memorization? \n\n2. Do you have any intuitions on why certain repetition cannot lead to attention sinks / large norm of activations? The paper about massive activations [1] also show that besides the first token, several other tokens with low semantics could also induce the attention sink. Do you think this is related?\n\n3. I am aware that theoretical analysis of equation 4 is intrinsically difficult, do you have any theoretical intuitions? \n\n[1] Sun et al. Massive Activations in Large Language Models. COLM 2024.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper explains the mechanism behind LLMs' failure to accurately repeat a single word multiple times by linking it to the "attention-sink" phenomenon. The authors identify a specific neural circuit where the first attention layer marks the initial token and a later MLP neuron adds high-magnitude values to its hidden state, creating the sink. This circuit is disrupted when processing long repetitions, causing the model to drift from instructions, sometimes leaking training data. All reviewers recommended acceptance, appreciating the paper's mechanistic interpretability approach, strong empirical evidence, and the proposed targeted patch that fixes the issue without harming model performance. During the rebuttal, the authors effectively addressed concerns about the theoretical foundations of their work, generalizability to other models, and the connection between attention sinks and training data leakage. Given the paper's novel contributions to understanding and mitigating a security vulnerability in LLMs, I recommend accepting this paper for ICML 2025.
Decision: Accept (poster)