Memorization Inheritance in Sequence-Level Knowledge Distillation for Neural Machine Translation
Authors: Verna Dankers, Vikas Raunak
Accepted at: acl25

Memorization Inheritance in Sequence-Level Knowledge Distillation for Neural Machine Translation

Verna Dankers*
University of Edinburgh
vernadankers@gmail.com

Vikas Raunak‡
Microsoft
viraunak@microsoft.com

Abstract
In this work, we explore how instance-level memorization in the teacher Neural Machine Translation (NMT) model gets inherited by the student model in sequence-level knowledge distillation (SeqKD). We find that despite not directly seeing the original training data, students memorize more than baseline models (models of the same size, trained on the original data)—3.4% for exact matches and 57% for extractive memorization—and show increased hallucination rates. Further, under this SeqKD setting, we also characterize how students behave on specific training data subgroups, such as subgroups with low quality or specific counterfactual memorization (CM) scores, and find that students exhibit greater denoising on low-quality subgroups. Finally, we propose a modification to SeqKD named Adaptive-SeqKD, which intervenes in SeqKD to reduce memorization and hallucinations. Overall, we recommend caution when applying SeqKD: students inherit both their teachers’ superior performance \emph{and} their fault modes, thereby requiring active monitoring.

1 Introduction
Memorization of noisy training data creates unexpected failure modes in \emph{neural machine translation} (NMT) models (Raunak and Menezes, 2022), thus presenting a reliability risk when deploying them in the real world. To make NMT models inference-friendly, they are often trained using \emph{sequence-level knowledge distillation} (SeqKD) (e.g., Bapna et al., 2022; Costa-jussà et al., 2022). This is a KD variant in which teachers generate synthetic targets for students (Hinton, 2014; Kim and Rush, 2016). SeqKD yields smaller student models whose performance is competitive with the teacher’s performance. Follow-up work has focused primarily on modifying SeqKD objectives to further improve NMT performance (e.g., Wen et al., 2023; Zhang et al., 2023; Wang et al., 2023, 2024). Apart from being used for model compression, SeqKD has proved very beneficial for low-resource and long-tail data (Dabre and Fujita, 2020; Currey et al., 2020; Gumma et al., 2023; Zhou et al., 2024; De Gibert et al., 2024) but its applications extend beyond that as well, e.g., to continual learning (Chuang et al., 2020; Zhao et al., 2022).

<table>
  <tr>
    <th>Extractive Memorization (ExMem) with respect to the initial parallel corpus increases 57.0%±15.4 in students compared to baselines (i.e. models memorized to emit the target even if we omit the italicized text):</th>
  </tr>
  <tr>
    <td>(1) \( s_C \) Reprezentacja Trynidadu i Tobago [w piłce nożnej]<br>
      \( t_T \) Trinidad and Tobago national football team<br>
      \( t_S \) Trinidad and Tobago national football team</td>
  </tr>
  <tr>
    <td>Students have 31.0%±25.7 more oscillatory hallucinations (in blue) than baselines:</td>
  </tr>
  <tr>
    <td>(2) \( s_C \) 1–5 , Stewards are appointed to publish the revelations (…)<br>
      \( t_T \) 1–5, Diener werden ernannt, um die Offenbarungen zu veröffentlichen (…)<br>
      \( t_S \) Die Heiligen sind in der Regel in der Regel (…)</td>
  </tr>
  <tr>
    <td>Students show secondary ExMem (ExMem with respect to the teacher-generated corpus):</td>
  </tr>
  <tr>
    <td>(3) \( s_C \) Electrical industry in Dominican [Republic - AmarillasLatinas.net]<br>
      \( t_T \) Elektrische Industrie in Dominikanische Republik<br>
      \( t_S \) Elektrische Industrie in Dominikanische Republik - AmarillasLatinas.net</td>
  </tr>
  <tr>
    <td>For low-quality source-target pairs, we observe amplified denoising in students:</td>
  </tr>
  <tr>
    <td>(4) \( s_C \) La fiche du Pikauba par la Fromagerie Hamel.<br>
      \( t_C \) Pule x Teuerster Käse der Welt aus Eselsmilch.<br>
      \( t_T \) Die Käserei Hamel in Pikauba. (Comet-QE-22=0.47)<br>
      \( t_S \) Die Geschichte des Pikauba durch die Hamel Käserei. (Comet-QE-22=0.62)<br>
      \( t_B \) Die Pikauba-Fassung wird von der Käserei Hamel betrieben. (Comet-QE-22=0.47)</td>
  </tr>
</table>

Figure 1: An illustration of our findings. Sources (\( s \)) are from the corpus (\( C \)); translations (\( t \)) are from teachers, students and baselines (\( T, S, B \)).
Yet, the understanding of SeqKD lags behind its usage. Prior work in this direction primarily studies why SeqKD is successful, attributing it to mode reduction of the training data (Zhou et al., 2020; Song et al., 2021), or suggesting that SeqKD acts as a regularization technique (Gordon and Duh, 2019). In this work, we better try to understand how model behavior gets transmitted from teacher to student, moving beyond only analyzing average-case performance. In particular, we focus on how the student inherits instance-level memorization. Recent work on image classification suggests that KD inhibits memorization in the student (Lukasik et al., 2024), but also that membership inference attacks on the student are often successful (Jagielski et al., 2024). However, the connection between memorization and SeqKD in NLP is new territory: characterizing this is imperative to mitigate memorization-related failures in students.

Our main contributions are as follows: (1) We provide a quantification of memorization inheritance in §2: we identify that even though SeqKD inhibits memorization from teacher to student, the student memorizes more about the initial parallel corpus and hallucinates more than it would have, had it been trained without SeqKD. (2) We perform subgroup analyses in §3, for data subsets with specific characteristics, such as examples of different quality levels or with specific counterfactual memorization scores (Feldman, 2020). We identify subgroups for which the student outperforms both the teacher and baseline models. (3) To reduce memorization and mitigate accentuated hallucinations we propose Adaptive-SeqKD in §4: a simple intervention in the SeqKD algorithm wherein we adapt the teacher by finetuning it briefly on intrinsically obtained high-quality data to reduce memorization and hallucinations in the student.

Figure 1 demonstrates a subset of these findings with examples from our datasets.

2 Memorization inheritance

2.1 Experimental setup

In SeqKD, teacher \( \theta_T \) is trained on a corpus with source sequences \( S_C \) and targets \( T_C \), and generates translations of \( S_C \) with beam size \( k \). The source sequences and the teacher-generated translations (\( S_C \) and \( T_T \)) form the training corpus for student \( \theta_S \). We use data from the WMT20 corpus (Barrault et al., 2020), for five language pairs: DE-EN and EN-DE (48M), PL-EN and EN-PL (12M), and FR-DE (14M). Appendix A provides more detail about the WMT corpora, and the validation and test data.

For all languages, a Transformer-large teacher trains for 300k steps on \( S_C \) and \( T_C \), followed by training a Transformer-base student for 100k steps on \( S_C \) and \( T_T \) (\( k=1 \)).\footnote{Training duration set to equal the setup of Vaswani et al. (2017). We train using MarianMT (Junczys-Dowmunt et al., 2018). For the full training setup see Appendix A and our codebase: https://github.com/vernadankers/memseqkd.} We also train Transformer-base directly on \( S_C \) and \( T_C \), to have a baseline (\( \theta_B \)) for what the student model would have memorized when exposed directly to WMT20 data. In Appendix C, we furthermore experiment with varying beam size \( k \) and the student’s model size.

Model quality metrics  We first examine the models’ quality, to ascertain the SeqKD framework works as intended. We report the following reference-based metrics for translations generated with beam size five: BLEU and chrF, Translation Error Rate (TER), and the Comet-20 and Comet-22 metrics that use neural methods for translation quality estimation (Rei et al., 2020, 2022). We supplement this with the reference-free metrics of Comet-QE-20, Comet-QE-22.

All metrics will be applied to WMT test data, and the reference-free methods are furthermore applied to translations of monolingual data: CommonCrawl data provided by Barrault et al. (2020), and data from the Pulpo poetry corpus (De la Rosa et al., 2023), to examine out-of-domain performance.

Memorization metrics  We quantify memorization by comparing greedily translated \( S_C \) to \( T_C \) for all models, and to \( T_T \), for \( \theta_S \). We measure the replication (exact match) rate, and the extractive memorization (ExMem, Raunak and Menezes, 2022) rate. ExMem finds examples for which models memorized to emit the target after seeing at most

![Performance of teacher, student and baseline models for four model quality metrics.](page_682_153_563_377.png)

Figure 2: Performance of teacher, student and baseline models for four model quality metrics.
75% of the source, e.g., see Example (1). The ExMem rate is the percentage of extractively memorized examples out of the replicated examples.

We also quantify the hallucination rate since hallucinations are often linked to models’ memorization capabilities (e.g., Guerreiro et al., 2023; McKenna et al., 2023). We employ binary metrics because they are high-precision and do not require setting a threshold in the absence of ground truth data (Guerreiro et al., 2023; Raunak et al., 2021). We measure the rates of natural hallucinations (NatHal) and oscillatory hallucinations (OscHal). NatHal is the percentage of source sequences that map to a translation that is repeated in the model’s translations at least five times. OscHal is the percentage of translations with bigrams repeated at least 10 times in the target but not the source.2

2.2 Results

General model quality Figure 2 and Appendix B provide performance differences for \( \theta_T, \theta_S \) and \( \theta_B \). Overall, \( \theta_T \) outperforms \( \theta_S \), and \( \theta_S \) outperforms \( \theta_B \). \( \theta_S \) and \( \theta_B \) merely differ in the training targets, which demonstrates that our SeqKD pipeline works as intended. The ordering of models in terms of their quality also holds for CommonCrawl and Pulpo data (see Table 3, Appendix B).

Figure 3: Memorization metrics for \( \theta_T, \theta_S \) and \( \theta_B \) and the percentual increase comparing \( \theta_S \) to \( \theta_B \).

Figure 4: Hallucination metrics for \( \theta_T, \theta_S \) and \( \theta_B \) and the percentual increase comparing \( \theta_S \) to \( \theta_B \).

SeqKD facilitates memorization Figure 3 summarizes the memorization results. If we first look at the replication rate, \( \theta_S \) replicates less from WMT20 than \( \theta_T \) but more than \( \theta_B \): students’ replication rate with respect to \( \mathcal{T}_C \) is 3.4%(±0.9) higher than for \( \theta_B \). Students also replicate original material from \( \theta_T \): the overall student replication rate for \( \mathcal{T}_T \) is 35.3%(±2.7) (see Table 4, Appendix B).

For the ExMem rate with respect to \( \mathcal{T}_C \) (Figure 3b), a similar pattern emerges, but with a starker difference between \( \theta_S \) and \( \theta_B \): students extractively memorize less from \( \mathcal{T}_C \) compared to \( \theta_T \), but more compared to \( \theta_B \), with a mean increase of 57.0%(±15.4). This is quite surprising; note that by definition, the rate reported here expresses how many of the replicated examples are extractively memorized. Students only observed 18.4% (on average) of \( \mathcal{T}_C \) through the SeqKD pipeline (the portion that \( \theta_T \) replicated) and yet within that smaller pool they still memorized more than \( \theta_B \), that was exposed to 100% of the corpus. Not only have students extractively memorized WMT20 examples (‘primary ExMem’), they also show ExMem with respect to \( \theta_T \) (‘secondary ExMem’, quantified in Table 4, Appendix B). Figure 3c provides the absolute numbers of ExMem examples, distinguishing primary from secondary ExMem that constitute 41% and 59% of all ExMem examples, respectively. Example (3) demonstrates secondary ExMem: the student has memorized to hallucinate “AmarillasLatinas.net” from \( \theta_T \)’s target when merely shown the source’s prefix, but \( \theta_T \) has not.3

Why would SeqKD facilitate memorization? We hypothesize that this is due to its denoising function: if noisy data acts as a regularizer during train-

2To improve the memorization/hallucination metrics’ precision, some training examples are excluded (see Appendix B, along with implementation details and hyperparameters).
3ExMem is a more widespread issue affecting commercial translation systems too, see Appendix E.
ing and \( \theta_T \) partly filters that noise through SeqKD, training \( \theta_S \) with reduced regularization could lead to increased memorization compared to \( \theta_B \). Reduced regularization is traditionally associated with increased memorization in machine learning. In §3, we demonstrate SeqKD’s denoising function.

SeqKD amplifies hallucinations It is not just memorization that is amplified through SeqKD; Figure 4 indicates that hallucinations are also amplified. For the oscillatory hallucinations, both \( \theta_S \) and \( \theta_B \) generate more of these than \( \theta_T \), and the student hallucinates more than \( \theta_B \), on average (an increase of 31.0%±25.7, with no increase observed for PL-EN). Appendix B presents additional results for monolingual corpora CommonCrawl and Pulpo. Across the board, \( \theta_S \) hallucinates most there, too.

For the natural hallucinations, \( \theta_S \) and \( \theta_B \) hallucinate less than \( \theta_T \), but students still hallucinate 13.8%(±5.0) more than \( \theta_B \).

3 Subgroup analysis

We now turn our attention to data subgroups to describe SeqKD’s impact on samples with specific characteristics. We compute replication metrics (exact match, chrF), neural quality metrics (Comet(-QE)-22), and textual diversity (MSTTR). Subgroups contain up to 10k examples, with the exception of the random group.

Subgroups We construct 12 subgroups: Firstly, we randomly sample 50k examples from each of the WMT20 corpora. Secondly, we construct five quality-based subgroups by bucketing WMT20 examples based on the Comet-QE-22 metric, yielding five subgroups (with ranges <0.2, 0.2-0.4, 0.4-0.6, ≥0.8). Thirdly, we create counterfactual memorization (CM) subgroups. Assuming input \( x \) and target \( y \), and a model with the parameters \( \theta^T \) trained on all training data, and \( \theta^{1st} \) trained on all examples except \( (x, y) \), CM\((x, y)=p_{\theta^T}(y|x) - p_{\theta^{1st}}(y|x)\) (Feldman, 2020; Feldman and Zhang, 2020). We approximate CM scores for all datapoints for EN-DE, and for 10% of the datapoints for the remaining language pairs, as detailed in Appendix F.1. We create four subgroups of examples (with ranges <0.2, 0.2-0.3, 0.3-0.4, ≥0.4). Finally, we create two confidence-based subgroups, by taking \( \theta_T \)'s log-probability averaged over the generated tokens for \( T_T \), selecting the top and bottom 10k examples.

Results We refer the reader to Appendix F for the full results, among which we consider the following patterns to be the most noteworthy:

(1) The random subgroup reflects memorization results: Exact and non-exact match-based metrics for this group (see chrF in Figure 9) follow the overall ranking of \( \theta_T > \theta_S > \theta_B \).

(2) Quality-based subgroups demonstrate (amplified) denoising: The lower the quality of the subgroup, the less models replicate from the corpus (Figure 5a,b). When we look at the Comet-QE-22 quality of the translations the models generate for low-quality subgroups, \( \theta_S \) and \( \theta_B \) perform better than \( \theta_T \), and \( \theta_S \) outperforms \( \theta_B \) (Figure 6). This effect is thus partially attributable to the capacity gap between large and base models, and partially to KD, and confirms prior work (e.g., Zhou et al., 2020) that suggested SeqKD has a denoising function: \( \theta_T \) denoised WMT20 for \( \theta_S \), and now \( \theta_S \) shows amplified denoising on that same data. Example (4) already illustrated this in the introduction.

(3) High counterfactual memorization examples are not replicated: Examples that have high CM for \( \theta_T \), do not stand out in terms of replication rates for students or baselines (Figure 5c), and show some amplified denoising (see Figure 6, although substantially less than low-quality subgroups).

(4) Low-confidence examples are not inherited: Finally, for examples for which \( \theta_T \) generated translations with very low confidence, \( \theta_S \) has low replication and strong amplified denoising (Figure 6).

![Illustration of how subgroups (indicated in sub-captions) vary in replication. Error bars indicate standard deviations over language pairs.](page_120_120_420_320.png)
Figure 5: Illustration of how subgroups (indicated in sub-captions) vary in replication. Error bars indicate standard deviations over language pairs.

![Comet-QE-22 increases compared to the teacher per subgroup, averaged over language pairs.](page_120_480_420_180.png)
Figure 6: Comet-QE-22 increases compared to the teacher per subgroup, averaged over language pairs.
<table>
  <tr>
    <th>Metric</th>
    <th>\( \theta_{T,hq} \)</th>
    <th>\( \theta_{T,ra} \)</th>
    <th>\( \theta_{S,hq} \)</th>
    <th>\( \theta_{S,ra} \)</th>
  </tr>
  <tr>
    <td>BLEU</td>
    <td>0.0±0.5</td>
    <td>-1.2±0.8</td>
    <td>-0.2±1.7</td>
    <td>-1.2±1.6</td>
  </tr>
  <tr>
    <td>C-QE-22</td>
    <td>+0.2±0.3</td>
    <td>-0.2±0.1</td>
    <td>+0.3±0.3</td>
    <td>-0.1±0.2</td>
  </tr>
  <tr>
    <td>ExMem \( T_C \)</td>
    <td>-13.1±10.2</td>
    <td>-7.5±9.3</td>
    <td>-23.7±24.0</td>
    <td>-11.9±29.5</td>
  </tr>
  <tr>
    <td>OscHal</td>
    <td>-55.1±11.3</td>
    <td>+7.7±11.2</td>
    <td>-58.9±7.5</td>
    <td>+9.8±15.6</td>
  </tr>
</table>

Table 1: Percentual performance change following Adaptive-SeqKD, compared to \( \theta_T \) and \( \theta_S \), using high-quality (\( hq \)) or random (\( ra \)) data.

4 Adaptive-SeqKD reduces memorization

We previously observed increased memorization and hallucinations in \( \theta_S \) compared to \( \theta_B \), but also amplified denoising on data subsets. We now investigate whether we can reduce students’ failures without harming performance otherwise. We apply Adaptive-SeqKD, which briefly finetunes the teacher on high-quality data before producing \( T_T \). Instead of costly external metrics like Comet-QE, we use intrinsic metrics for data quality by selecting 500k sequences where \( \theta_T \) nearly memorized the target (chrF>90), \( \theta_T \) is confident (normalized translation score > 0.9), and source lengths exceed 5 tokens. We finetuned \( \theta_T \) per language pair for 200 steps and compared it to finetuning on a random 500k sample meeting the length requirement.

Table 1 shows that teachers finetuned on the high-quality data (and their students) perform similar in terms of BLEU and Comet-QE, but much better in terms of ExMem and OscHal. Finetuning on random data reduced ExMem but not hallucinations. We include additional metrics in Appendix D.

Although we applied Adaptive-SeqKD here by finetuning on a training data subset, the technique could be modified for scenarios in which the training data is unknown. For instance, one could simply take a high-quality subset from a new source and finetune models on it prior to running SeqKD.

5 Related work

Memorization in NLP and NMT  Memorization as a general topic has seen increased attention in NLP in recent years. A wide range of work examined how many examples large language models memorize and how to extract those memories (e.g. Carlini et al., 2021, 2023; Nasr et al., 2025), and identified how characteristics of datapoints, models and training techniques relate to memorization (e.g. Mireshghallah et al., 2022; Biderman et al., 2024; Prashanth et al., 2024; Leschi et al., 2024; Li et al., 2024). Alternative lines of related work focused on how memorization affects models internally, for instance, for factual memories (e.g. Geva et al., 2023) or idiomatic expressions (Haviv et al., 2023).

Only a small subset of related work investigated memorization in the NMT context: Raunak et al. (2021) identified that examples with high CM scores tend to elicit hallucinations from models more easily than other examples. In later work, Raunak and Menezes (2022) were the first to discuss the phenomenon of ExMem for NMT systems. Lastly, Dankers et al. (2023) examined the connection between datapoints’ features and CM scores in NMT, and emphasized that memorization can sometimes benefit performance.

Knowledge distillation and memorization  The connection to memorization and KD had, thus far, only been investigated for the vision domain, where Jagielski et al. (2024) studied membership inference attacks for image classifiers trained through distillation. Although distillation improved the average-case privacy (i.e. reduces memorization), the most vulnerable examples barely benefited from KD. Lukasik et al. (2024) studied CM of image classifiers, concluding that distillation inhibits CM. Some of our findings echo these results, namely that compared to \( \theta_T \), memorization is indeed dampened in the student, and that high-CM examples do not stand out in terms of replication.

6 Conclusion

SeqKD is popular for effectively training smaller NMT models, but we show that it also introduces issues like worsened ExMem and hallucinations in \( \theta_S \) compared to \( \theta_B \). At the same time, the subgroup analyses showed that teachers’ CM examples are not necessarily replicated by \( \theta_S \), and that students exhibit amplified denoising on low-quality examples. This highlights a paradox: through SeqKD, students memorize more about the corpus than \( \theta_B \), yet also outperform \( \theta_B \) and \( \theta_T \) on data where they did not memorize. Student improvements thus happen both by mimicking \( \theta_T \), and by deviating from \( \theta_T \). Future work could suppress memorization during SeqKD, refine Adaptive-SeqKD, and adjust hyperparameters4 to create more robust SeqKD pipelines. We advise caution with SeqKD: students may inherit not only the teacher’s strengths but also its failures, requiring careful monitoring beyond average-case performance.

4Increasing k reduces the student’s OscHal rate to below that of \( \theta_B \), in particular, with Adaptive-SeqKD yielding further improvements (see Appendix C).
7 Limitations

We identify the following three limitations with our work:

• Limited experimental setup: inherent to selecting one (although common) experimental setup of distilling a large model into a smaller model, is that the findings need not necessarily transfer to other settings. We experimented with multiple language pairs, and also varied the beam size and student size in Appendix C to comment on this limitation, but recognize that there are other settings that would be interesting to study, such as distilling translation models from LLMs. We opted for the more common SeqKD setup, because of its popularity in the years past: most deployed translation systems are not LLM-based (yet). Besides, SeqKD as a technique is still alive and kicking as demonstrated by, for instance, the recently developed OpusDistillery library5 (De Gibert et al., 2025).

• Niche phenomena: ExMem and hallucinations are phenomena one would only rarely encounter when interacting with NMT systems (Raunak and Menezes, 2022). We consider them worthy of investigation, nonetheless, because they represent extreme system failure that goes far beyond a simple mistranslation. Particularly for high-resource languages, NMT performance is approaching a ceiling, thanks to the sheer volume of translation examples available. Because NMT test performance is so high, it is important that NMT practitioners go beyond standard evaluation and look into domain-specific phenomena, long-tail phenomena (e.g. Raunak et al., 2022), and robustness failures. ExMem and hallucinations represent such failures.

• Reliability issues not yet solved: Adaptive-SeqKD substantially reduced the ExMem and hallucination rates, but did not resolve the issue altogether. We conducted limited experimentation in perfecting Adaptive-SeqKD and recognize this could be further expanded upon in the future.

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A  Data and experimental setup

WMT data  We download the parallel corpora from the WMT20 website, using sources listed in Table 2. For EN-DE we use the validation/test data from Raunak and Menezes (2022), which is a combination of WMT test data from recent years. For the other language pairs, we use the WMT20 test data, along with the WMT19 test data for validation during training. We honor the licensing terms by using the WMT data for research purposes only and citing the shared task article.

Additional data  We run monolingual model evaluation using data from additional sources:
1. 1M CommonCrawl examples, sampled from the first 100M monolingual CommonCrawl datapoints provided by WMT20. To the best of our knowledge, our use is in line with CC’s terms of use.
2. Up to 1M Pulpo examples, from De la Rosa et al. (2023)’s multilingual Prolific Unannotated Literary Poetry Corpus containing verses and stanzas. Pulpo contains monolingual sequences for all language pairs, apart from monolingual Polish data. We selected the data because it is expected to be out-of-distribution compared to the WMT20 training corpora. Pulpo contains poems that are copyright-free, or distributed under permissive licenses (De la Rosa et al., 2023, p.3).

Training and evaluation  Models are trained using the marian toolkit (v1.12.16), using the setup of Raunak and Menezes (2022), that mimics the hyperparameters of Vaswani et al. (2017). \( \theta_T \) trains for 300k steps; \( \theta_S \) and \( \theta_B \) train for 100k steps. We use 8 Tesla V100-SXM2-32GB GPUs for model training. When evaluating translations using Comet, we use models wmt20-comet-da, wmt22-comet-da, wmt20-comet-qe-da and wmt22-cometkiwi-da, using Comet v1.2.0. Visit our git repository for the training, evaluation and visualization code.

<table>
  <tr>
    <th>Source</th>
    <th>EN-DE</th>
    <th>FR-DE</th>
    <th>PL-EN</th>
  </tr>
  <tr>
    <td>Europarl</td>
    <td>1.8M</td>
    <td>1.8M</td>
    <td>632k</td>
  </tr>
  <tr>
    <td>ParaCrawl</td>
    <td>34.4M</td>
    <td>7.2M</td>
    <td>6.6M</td>
  </tr>
  <tr>
    <td>Common Crawl</td>
    <td>2.4M</td>
    <td>622k</td>
    <td>-</td>
  </tr>
  <tr>
    <td>News Commentary</td>
    <td>362k</td>
    <td>284k</td>
    <td>-</td>
  </tr>
  <tr>
    <td>Wiki Titles</td>
    <td>1.4M</td>
    <td>942k</td>
    <td>1.0M</td>
  </tr>
  <tr>
    <td>Tilde Rapid corpus</td>
    <td>1.6M</td>
    <td>-</td>
    <td>278k</td>
  </tr>
  <tr>
    <td>WikiMatrix</td>
    <td>6.2M</td>
    <td>3.4M</td>
    <td>3.1M</td>
  </tr>
</table>

Table 2: Composition of the 3 parallel corpora.

B  Model quality and memorization

Model quality metrics  Table 3 provides model quality metrics for all language pairs. Generally, \( \theta_T > \theta_S > \theta_B \) (except for TER, that should be minimized instead of maximized), and where the results differ we italicized the numbers; this mostly happens for the monolingual CommonCrawl data. Yet, across the board, the pattern is clear for both WMT20 and monolingual data. The Pulpo scores are noticeably lower for both Comet-QE metrics. This could indicate that the poetry data is harder to translate, but we cannot reliably make a direct comparison across domains. Most importantly, the system ranking holds for this OOD data, too.

<table>
  <tr>
    <th rowspan="2">Lang.</th>
    <th colspan="5">WMT20</th>
    <th colspan="5">CommonCrawl</th>
    <th colspan="5">Pulpo</th>
  </tr>
  <tr>
    <th>BLEU</th>
    <th>chrF</th>
    <th>TER</th>
    <th>Com-20</th>
    <th>Com-22</th>
    <th>Com-QE-20</th>
    <th>Com-QE-22</th>
    <th>Com-QE-20</th>
    <th>Com-QE-22</th>
    <th>Com-QE-20</th>
    <th>Com-QE-22</th>
    <th>Com-QE-20</th>
    <th>Com-QE-22</th>
  </tr>
  <tr>
    <td rowspan="3">EN-DE</td>
    <td>\( \theta_T \)</td>
    <td>33.11</td>
    <td>61.46</td>
    <td>54.04</td>
    <td>41.77</td>
    <td>81.73</td>
    <td>31.52</td>
    <td>80.37</td>
    <td>35.39</td>
    <td>73.11</td>
    <td>11.56</td>
    <td>65.83</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>32.49</td>
    <td>61.01</td>
    <td>54.31</td>
    <td>42.00</td>
    <td>81.66</td>
    <td>31.11</td>
    <td>80.85</td>
    <td>34.93</td>
    <td>74.00</td>
    <td>10.86</td>
    <td>65.37</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>30.15</td>
    <td>59.41</td>
    <td>56.44</td>
    <td>34.35</td>
    <td>79.42</td>
    <td>28.08</td>
    <td>78.99</td>
    <td>34.77</td>
    <td>72.97</td>
    <td>10.58</td>
    <td>63.82</td>
  </tr>
  <tr>
    <td rowspan="3">DE-EN</td>
    <td>\( \theta_T \)</td>
    <td>37.49</td>
    <td>64.95</td>
    <td>48.70</td>
    <td>68.97</td>
    <td>87.42</td>
    <td>49.40</td>
    <td>84.28</td>
    <td>38.19</td>
    <td>77.01</td>
    <td>9.46</td>
    <td>64.72</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>35.36</td>
    <td>63.65</td>
    <td>50.65</td>
    <td>65.87</td>
    <td>86.46</td>
    <td>46.27</td>
    <td>83.55</td>
    <td>37.02</td>
    <td>76.77</td>
    <td>8.37</td>
    <td>63.29</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>34.18</td>
    <td>63.11</td>
    <td>52.20</td>
    <td>64.68</td>
    <td>86.11</td>
    <td>45.39</td>
    <td>83.32</td>
    <td>37.02</td>
    <td>76.55</td>
    <td>8.36</td>
    <td>62.54</td>
  </tr>
  <tr>
    <td rowspan="3">PL-EN</td>
    <td>\( \theta_T \)</td>
    <td>31.98</td>
    <td>59.14</td>
    <td>53.66</td>
    <td>54.20</td>
    <td>82.59</td>
    <td>34.70</td>
    <td>77.82</td>
    <td>34.35</td>
    <td>74.58</td>
    <td>n/a</td>
    <td>n/a</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>31.41</td>
    <td>58.95</td>
    <td>53.99</td>
    <td>51.83</td>
    <td>82.08</td>
    <td>32.54</td>
    <td>77.21</td>
    <td>34.08</td>
    <td>74.72</td>
    <td>n/a</td>
    <td>n/a</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>30.69</td>
    <td>58.58</td>
    <td>54.44</td>
    <td>50.55</td>
    <td>81.69</td>
    <td>32.23</td>
    <td>76.88</td>
    <td>34.33</td>
    <td>74.44</td>
    <td>n/a</td>
    <td>n/a</td>
  </tr>
  <tr>
    <td rowspan="3">EN-PL</td>
    <td>\( \theta_T \)</td>
    <td>31.04</td>
    <td>59.86</td>
    <td>56.91</td>
    <td>92.07</td>
    <td>90.59</td>
    <td>74.56</td>
    <td>83.93</td>
    <td>33.71</td>
    <td>70.20</td>
    <td>12.24</td>
    <td>61.58</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>30.13</td>
    <td>58.98</td>
    <td>57.91</td>
    <td>89.09</td>
    <td>89.77</td>
    <td>71.99</td>
    <td>83.53</td>
    <td>34.18</td>
    <td>71.08</td>
    <td>11.46</td>
    <td>61.16</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>28.94</td>
    <td>58.27</td>
    <td>59.21</td>
    <td>86.52</td>
    <td>89.17</td>
    <td>70.41</td>
    <td>83.01</td>
    <td>34.18</td>
    <td>70.09</td>
    <td>10.94</td>
    <td>59.55</td>
  </tr>
  <tr>
    <td rowspan="3">Fr-DE</td>
    <td>\( \theta_T \)</td>
    <td>28.86</td>
    <td>60.70</td>
    <td>60.94</td>
    <td>60.16</td>
    <td>86.67</td>
    <td>48.24</td>
    <td>84.23</td>
    <td>33.86</td>
    <td>72.24</td>
    <td>9.89</td>
    <td>58.44</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>27.77</td>
    <td>60.22</td>
    <td>62.31</td>
    <td>57.08</td>
    <td>85.71</td>
    <td>46.60</td>
    <td>83.66</td>
    <td>33.21</td>
    <td>72.04</td>
    <td>9.42</td>
    <td>58.38</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>26.99</td>
    <td>59.69</td>
    <td>63.30</td>
    <td>55.95</td>
    <td>85.34</td>
    <td>46.06</td>
    <td>83.47</td>
    <td>32.90</td>
    <td>71.58</td>
    <td>9.03</td>
    <td>57.30</td>
  </tr>
</table>

Table 3: Model performance for the five language pairs, using the experimental setup described in §2.1.
Memorization-related metrics  Table 4 provides memorization metrics for all language pairs. For replication, ExMem and NatHal rates, it holds that \( \theta_T > \theta_S > \theta_B \), whereas for oscillatory hallucinations, \( \theta_S \) typically hallucinates most, although there are exceptions (highlighted in italics).

<table>
  <tr>
    <th rowspan="2">Lang.</th>
    <th rowspan="2">\( \theta \)</th>
    <th colspan="3">WMT20</th>
    <th colspan="3">CommonCrawl</th>
    <th colspan="2">Pulpo</th>
  </tr>
  <tr>
    <th>Replic. \( \mathcal{T}_C \)</th>
    <th>Replic. \( \mathcal{T}_T \)</th>
    <th>ExMem \( \mathcal{T}_C \) (#)</th>
    <th>ExMem \( \mathcal{T}_T \) (#)</th>
    <th>NatHal</th>
    <th>OscHal</th>
    <th>OscHal</th>
    <th>OscHal</th>
  </tr>
  <tr>
    <td rowspan="3">EN-DE</td>
    <td>\( \theta_T \)</td>
    <td>12.75</td>
    <td>n/a</td>
    <td>0.875 (36k)</td>
    <td>n/a</td>
    <td>0.699</td>
    <td>0.012</td>
    <td>0.020</td>
    <td><i>0.008</i></td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>11.12</td>
    <td>32.75</td>
    <td>0.627 (22k)</td>
    <td>0.397 (49k)</td>
    <td>0.648</td>
    <td>0.021</td>
    <td>0.029</td>
    <td><i>0.005</i></td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>10.65</td>
    <td>n/a</td>
    <td>0.356 (12k)</td>
    <td>n/a</td>
    <td>0.559</td>
    <td>0.014</td>
    <td>0.021</td>
    <td><i>0.009</i></td>
  </tr>
  <tr>
    <td rowspan="3">DE-EN</td>
    <td>\( \theta_T \)</td>
    <td>13.53</td>
    <td>n/a</td>
    <td>0.590 (27k)</td>
    <td>n/a</td>
    <td>0.614</td>
    <td>0.020</td>
    <td>0.024</td>
    <td><i>0.062</i></td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>11.80</td>
    <td>35.17</td>
    <td>0.446 (17k)</td>
    <td>0.246 (34k)</td>
    <td>0.583</td>
    <td>0.032</td>
    <td>0.037</td>
    <td><i>0.048</i></td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>11.29</td>
    <td>n/a</td>
    <td>0.320 (12k)</td>
    <td>n/a</td>
    <td>0.487</td>
    <td>0.023</td>
    <td>0.026</td>
    <td><i>0.024</i></td>
  </tr>
  <tr>
    <td rowspan="3">PL-EN</td>
    <td>\( \theta_T \)</td>
    <td>20.31</td>
    <td>n/a</td>
    <td>3.744 (55k)</td>
    <td>n/a</td>
    <td>1.203</td>
    <td>0.021</td>
    <td><i>0.106</i></td>
    <td>n/a</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>16.73</td>
    <td>34.65</td>
    <td>1.433 (17k)</td>
    <td>3.143 (79k)</td>
    <td>1.192</td>
    <td>0.044</td>
    <td><i>0.135</i></td>
    <td>n/a</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>16.25</td>
    <td>n/a</td>
    <td>0.836 (9k)</td>
    <td>n/a</td>
    <td>1.107</td>
    <td>0.048</td>
    <td><i>0.136</i></td>
    <td>n/a</td>
  </tr>
  <tr>
    <td rowspan="3">EN-PL</td>
    <td>\( \theta_T \)</td>
    <td>20.56</td>
    <td>n/a</td>
    <td>4.736 (63k)</td>
    <td>n/a</td>
    <td>1.737</td>
    <td>0.016</td>
    <td><i>0.078</i></td>
    <td>0.017</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>17.02</td>
    <td>33.64</td>
    <td>3.347 (35k)</td>
    <td>4.126 (94k)</td>
    <td>1.737</td>
    <td>0.037</td>
    <td><i>0.100</i></td>
    <td>0.029</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>16.52</td>
    <td>n/a</td>
    <td>2.394 (24k)</td>
    <td>n/a</td>
    <td>1.610</td>
    <td>0.033</td>
    <td><i>0.126</i></td>
    <td>0.027</td>
  </tr>
  <tr>
    <td rowspan="3">FR-DE</td>
    <td>\( \theta_T \)</td>
    <td>24.62</td>
    <td>n/a</td>
    <td>0.546 (10k)</td>
    <td>n/a</td>
    <td>0.799</td>
    <td>0.018</td>
    <td>0.025</td>
    <td>0.190</td>
  </tr>
  <tr>
    <td>\( \theta_S \)</td>
    <td>21.88</td>
    <td>40.39</td>
    <td>0.407 (6k)</td>
    <td>0.330 (13k)</td>
    <td>0.784</td>
    <td>0.073</td>
    <td>0.082</td>
    <td>0.223</td>
  </tr>
  <tr>
    <td>\( \theta_B \)</td>
    <td>21.39</td>
    <td>n/a</td>
    <td>0.257 (4k)</td>
    <td>n/a</td>
    <td>0.666</td>
    <td>0.045</td>
    <td>0.046</td>
    <td>0.081</td>
  </tr>
</table>

Table 4: Memorization metrics for the five language pairs, using the experimental setup described in §2.1.

Additional information on ExMem and hallucination metrics  To improve the precision of the ExMem and hallucination metrics, some groups of examples are excluded from the computation:
• ExMem: in some cases, it is justified that the model emits the target after having processed only 75% of the source, e.g., if the target paraphrases the source. To improve the precision, we thus exclude the following examples when computing ExMem: a) examples with a source shorter than 4 words, b) examples with incorrect source or target languages, c) examples for which the length ratios between source and target are over 1.3, d) examples for which the source equals the target.
• OscHal: examples with source sequences of at least 50 white-space tokenized tokens are excluded, because it becomes more likely that the repeated bigrams might be accurate for longer sequences. This only concerns a small portion of the training data, e.g., only 3.4% for EN-DE. We count a sequence as a hallucination if the most frequent bigram appears more than 10 times in the translation, and at least 4 times more often than in the source. We experimentally verified that when reducing that maximum count of 10, the OscHal rate increases, but the model ranking remains the same.
• NatHal: for the natural hallucinations, we exclude examples for which the Comet-QE-22 score for the source and the generated translation is above 0.85. This is to exclude cases where natural hallucinations are detected simply because there are source sequences that are each other’s paraphrase, for instance if both “Thank you for your visit at our website.” and “Thanks for visiting our website.” map to “Vielen Dank für Ihren Besuch auf unserer Website.”.

C  Varying SeqKD hyperparameters

Figure 7 demonstrates how model quality and memorization metrics change when we increase the beam size used to decode \( \mathcal{T}_T \) (for both FR-DE and EN-DE), or change the student’s model size (for EN-DE). \( \theta_{S_L} \) and \( \theta_{S_S} \) have hidden dimensionalities of 1024 and 256, respectively (with corresponding feedforward sizes of 4096 and 1024). When increasing the beam size: i) the quality of the students’ translations decrease (for EN-DE) or remain mostly stable (for FR-DE), ii) the replication rates slightly increase, and iii) ExMem decreases, although not consistently: for FR-DE, the students’ ExMem rates all exceed the baseline, whereas for DE-EN and \( k \in \{2, 5\} \) the students are slightly below \( \theta_B \). This is still concerning, considering that students were exposed to 18.4% (on average) of the original corpus, whereas the baseline has seen 100%, so even similar ExMem rates between \( \theta_S \) and \( \theta_B \) suggest KD facilitates memorization. The NatHal rate slightly reduces but still far exceeds the baseline. The only real improvement larger beam sizes appear to make are reducing OscHal, both on WMT20 data (OscHal), and on external data
Figure 7: Illustration of how model quality and memorization metrics change as a result of changing the SeqKD beam size for EN-DE.

(OscHal CommonCrawl). With slight performance reduction in EN-DE and no performance reduction in FR-DE this yields a simple KD lesson: even if beam search is more computationally expensive (particularly when applied to the entire training corpus): do not use greedy search in KD.

Reducing the student’s model size reduces the model’s translation quality, and reduces replication, ExMem and NatHal, but still yields similar OscHal rates. The larger model produces better translations than its teacher and has lower ExMem, but also increased hallucinations.

D Adaptive-SeqKD

Figure 8 displays how performance changes per language pair due to Adaptive-SeqKD. Changes to quality metrics like BLEU and Comet(-QE)-22 are relatively minor, but the ExMem and hallucination rates are strongly affected. Finetuning with the high-quality data (hq in the graphs) decreases ExMem (for four of five language pairs) and hallucinations (all language pairs); finetuning with random data (ra in the graphs) is somewhat effective in reducing ExMem, but mostly fails to effectively reduce the hallucination rates.

In the previous section, we observed that increasing the SeqKD beam size is beneficial for the reduction of the hallucination rate. We thus reran finetuning with high-quality data for \( S_{k=5} \) for EN-DE to examine whether finetuning also helps for an increased beam size. Upon doing so, the OscHal rate reduced with 28% for the WMT20 data and 33% for the CommonCrawl data, and the NatHat rate reduced with 10% for the WMT20 data, suggesting the wider applicability of Adaptive-SeqKD.

Figure 8: Performance changes observed for the different language pairs when applying Adaptive-SeqKD. Changes are computed as percentages with respect to the original teacher \( \theta_T \) and \( \theta_S \).
E  Extractive memorization in commercial systems

In the main paper, we discussed ExMem for the translation systems we trained, but it should be noted that ExMem does not only exist for sytems trained for academic purposes. To assert this, we assessed closed-source translation systems (Google Translate, DeepL and Microsoft Bing Translator),6 using 70 Europarl examples that led to ExMem for our WMT20 English-German models. ExMem is defined with respect to the training targets, and we do not know what closed-source translation systems are trained on. Still, because of how widespread Europarl is in translation corpora, it may have been a part of the training material for closed-source systems, too. For DeepL, the system outputs the target translation without having processed the full source sequence for 25 of those 70 examples. For Google Translate and Microsoft Bing Translator, that applies to 16 and 20 examples, respectively.

Four examples are provided below. In each source text below, if we omit the italicised part in square brackets, the translations remain the same. Consider the following two examples from DeepL:

(5) s  I have received seven motions for resolution tabled in accordance with Rule 103(2) [of the Rules of Procedure]
    t  Ich habe sieben Entschließungsanträge erhalten, die gemäß Artikel 103 Absatz 2 der Geschäftsordnung eingereicht wurden

(6) s  I have therefore abstained [from the vote]
    t  Ich habe mich daher der Stimme enthalten

Consider the following example from Microsoft Bing Translator:

(7) s  Mr President, the opposite is [the case]
    t  Herr Präsident, das Gegenteil ist der Fall

Consider the following example from Google Translate:

(8) s  Mr President, Madam Commissioner, ladies [and gentlemen]
    t  Herr Präsident, Frau Kommissarin, meine Damen und Herren

These are examples copied directly from Europarl, but the underlying robustness issue also pops up when using non-exact variants – e.g. consider the following variant of Example (8): “A cleaner, a teacher and a commissioner said: ladies and”. Google Translate still translates this including “Meine Damen und Herren” in its translation. It is hard to determine in which other domains ExMem occurs for these models, because we do not have a lot of insight into their training data, but that it is not something that only applies to our models, is for certain.

It is vital that we learn to understand what causes this type of memorization and how to avoid it. We identified knowledge distillation as contributing to exacerbating these types of memorization. We hope our insights can help both scientists and practitioners tackle such problems across different production systems as well, especially since deployed systems tend to be distilled (student) models.

F  Subgroup analyses

Before elaborating on the results for the subgroup analysis, we more elaborately explain how we approximated the CM scores.

F.1  Composing the counterfactual memorization subgroups

Assuming input \( x \) and target \( y \), and a model with the parameters \( \theta^{\text{tr}} \) trained on all training data, and \( \theta^{\text{st}} \) trained on all examples except \( (x, y) \), CM can be computed as follows (Feldman, 2020; Feldman and Zhang, 2020):

\[
\mathrm{CM}(x, y) = \underbrace{p_{\theta^{\text{st}}}(y|x)}_{\text{IN}} - \underbrace{p_{\theta^{\text{tr}}}(y|x)}_{\text{OUT}}
\]

6As accessed on the 30th of March, 2025. For Microsoft Bing Translator, the ‘formal tone’ was used for translation.
Leaving out individual datapoints, as this equation suggests, is computationally too expensive given the vast sizes of our WMT20 datasets. We, therefore, approximate CM scores for all datapoints for EN-DE, and for 10% of the datapoints for the remaining language pairs. Following Dankers et al. (2023, p.3), who previously computed CM scores for NMT examples, we replace the probability of the full target with the geometric mean of the target token probabilities. This is more robust to length differences than the full target probability.

For EN-DE, we approximate CM by training 10 teacher models on a randomly sampled 50% of the training corpus, while evaluating it on the remaining 50%, such that for each datapoint, the ‘IN’ and ‘OUT’ quantities in this equation are both estimated using five models.

For the remaining language pairs, we use the original \( \theta_T \) model to estimate the ‘IN’ quantity, and train another model according to the same training procedure on 90% of the training data, to estimate the ‘OUT’ quantity for 10% of the remaining training data. This is a rather coarse estimation, but should suffice to determine generic relations between CM and our evaluation metrics of interest.

Using the CM scores, we create six subgroups of interest. The last four were also contained in the main paper, and here we add the first two, that separate examples with a low CM score into two groups:
• IN and OUT performances \( \leq 0.2 \), marked ‘L(ow)-L(ow)’ in the figures;
• IN and OUT performances \( \geq 0.8 \), marked ‘H(igh)-H(igh)’ in the figures;
• \( \{ [0, 0.2), [0.2, 0.3), [0.3, 0.4), [0.4, 1.0] \} \).

F.2 Results

Random subgroup  Our baseline subgroup consists of 50k examples randomly sampled from each of the WMT20 corpora. Replication metrics (chrF in Figure 9) follow the overall patterns of \( \theta_T > \theta_S > \theta_B \), consolidating that SeqKD dampens memorization in \( \theta_S \) compared to \( \theta_T \) but amplifies it compared to \( \theta_B \). The quality and diversity metrics (Comet-QE-22 and MSTTR in Figure 9) emphasize that, compared to the corpus (column \( T_C \)), the models generate higher-quality translations, but with lower textual diversity.

![Evaluation metrics applied to the random subgroup, for all five language pairs. The square marker indicates the mean and standard deviation.](page_176_1012_1096_246.png)

Figure 9: Evaluation metrics applied to the random subgroup, for all five language pairs. The square marker indicates the mean and standard deviation.

Quality-based subgroups  How does KD affect examples from the WMT20 corpus with a certain quality? To categorize WMT20 examples based on quality, we applied the Comet-QE-22 metric using the corpus’s targets as translations (because the reference is now the translation, we apply Comet’s ‘reference-free’ method). We examine five subgroups: \( \{ [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), [0.8, 1.0] \} \).

In Figure 10, we first notice that for the low-quality subgroups, fewer translations are replicated from \( T_C \) (all models) and from \( T_T \) (for \( \theta_S \), see Figure 5b). Secondly, for groups with a quality below 0.6, both \( \theta_S \) and \( \theta_B \) generated better translations than \( \theta_T \), as per Comet-QE-22 applied to the model-generated translations. Textual diversity metrics here mostly follow the same trend as Comet-QE-22, with \( \theta_S \) showing the highest diversity.

Figure 13 visualizes the relative differences of students/baselines to the teacher more explicitly, as a percentual increase. For Comet-QE-22, if the student’s bar exceeds the baseline’s bar, this signifies that the improvement over the teacher is partially attributable to the capacity gap between large and base models, and partially to the SeqKD process. This improvement over the teacher holds for the lower quality groups (0-0.2, 0.2-0.4, 0.4-0.6), but not for the higher quality groups. Example (4) from the introduction already illustrated a sample from the lowest quality subgroup: in the corpus, the target was likely misaligned. \( \theta_T \)'s
translation is slightly better but still wrong, and \( \theta_S \)'s translation is slightly better than \( \theta_B \)'s. Compared to \( \theta_B \), \( \theta_S \) benefits from being presented with the teacher’s corpus, which is a denoised version of WMT20, since \( \theta_T \) replicates the least for the lowest-quality (noisiest) examples. \( \theta_S \), in turn, replicates less from \( T_T \) for these lowest-quality subgroups, too, which allows for amplified denoising compared to \( \theta_T \).

![Evaluation metrics applied to the quality subgroups, aggregated over language pairs with error bars indicating standard deviation over language pairs.](page_184_370_1082_246.png)

Figure 10: Evaluation metrics applied to the quality subgroups, aggregated over language pairs with error bars indicating standard deviation over language pairs.

Counterfactual memorization subgroups  If we now focus on the high CM subgroups (introduced in the previous subsection) in Figure 11, we firstly observe that in terms of memorization metrics, the teacher shows increased replication and chrF. This follows from the definition of CM as the IN metric is expected to correlate highly with replication: the higher the target probability, the more likely it is that the teacher can replicate the target when generating translations. We note, however, that the student and baseline replicate examples with high CM less than examples with lower CM. This is likely due to their lower capacity: by definition, CM highlights examples for which a model assigns a low probability to the target when that example is not in the training set, thus requiring more capacity/parameters from the original \( \theta_T \) to learn that target. The reduced replication also leads to lower Comet-22 scores for these examples, since that metric partially relies on the corpus’s target.

When looking at the Comet-QE-22 scores, we observe that the higher CM groups typically also have lower Comet-QE-22 scores, although the quality scores are still above the lowest quality subgroup discussed in the previous paragraph. The baseline and student models outperform \( \theta_T \) here, likely because they struggle to replicate the somewhat noisy targets as well as the teacher did. The student shows some amplified denoising compared to \( \theta_B \), but that does apply to CM groups across the board, and does not seem specific to individual CM subgroups.

All three models show more textual diversity for subgroups with higher CM scores.

Finally, if we inspect the two groups with very low and very high IN and OUT scores, the models—as expected based on the CM definition—do not replicate the ‘low’ group, and have very high replication rates for the ‘high’ group. For the remaining metrics, the ‘low’ group underscores the findings we previously reported for the low-quality subgroups: the student model shows amplified denoising, as reflected by the Comet-QE-22 metric, and also shows more textual diversity than the other two models. Examples that score badly in terms of both IN and OUT performance are typically low-quality, misaligned examples (Dankers et al., 2023); if the teacher does not replicate those low-quality targets, but partially denoises by translating them more accurately than the corpus did, the student can further improve upon that.

![Evaluation metrics applied to the CM subgroups, aggregated over language pairs with error bars indicating standard deviation over language pairs.](page_184_1532_1082_246.png)

Figure 11: Evaluation metrics applied to the CM subgroups, aggregated over language pairs with error bars indicating standard deviation over language pairs.
Confidence subgroups  Finally, we turn to the confidence-based groups, created using the lowest 10k and highest 10k examples based on the mean token-level log-probability of the translations the teacher generated. Figure 12 demonstrates that the low confidence examples stand out for all metrics: none of the models replicate their targets, all three models generate translations with a relatively low Comet-QE-22 score (akin to the lowest-quality subgroups discussed above), and all three models show an exceptionally low MSTTR. If we inspect the examples, they contain many misaligned examples from the corpus for which the teacher also does not generate an adequate translation (e.g., Example (9)), and a lot of cases where the teacher hallucinates in its translation (e.g., Example (10)). The student’s improvement here thus does not stem from learning from the superior targets \( \theta_T \) generated but more likely from deviating from \( \theta_T \). Yet, note that even if \( \theta_S \) does not necessarily copy these hallucinations from its teacher, we did find that the students inspected in the main paper showed amplified hallucination capabilities, so low-quality teacher targets like this could have other unanticipated effects downstream.

(9) \( s_C \) Evergreen Terrace - Almost Home 22.
\( t_C \) Zum Ernst-Gettk-Haus, Hausnummer 68, siehe unten.
\( t_T \) Die alte Burganlage – heute noch 22 Hektar.
\( t_S \) Die Evergreen Terrace ist fast die Heimat 22.
\( t_B \) In: Evergreen Terrace – Fast Home 22.

(10) \( s_C \) Because the race is restricted to Canadian-bred horses, it is not eligible for grading, despite being one of Canada’s most prestigious races Northern Dancer Turf Stakes, (...)
\( t_C \) Kammerherr, Land- und Obergerichtsrat Magnus Graf von Moltke, Ständedeputierter der Stadt Schleswig, Präsident der konstituierenden Ständesversammlung des Jahres 1836, (...)
\( t_T \) Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, (...)
\( t_S \) Depots of Canada’hintergrund für kanadische Pferde, ist es nicht berechtigt für das Einstufung, obwohl es eines der renommiertesten Rennen Northern Dancer Turf Stakes, (...)
\( t_B \) Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, Stakes, (...)

![Five boxplots comparing different student translation techniques under varying teacher confidences for various metrics.](page_570_666_1148_384.png)

Figure 12: Evaluation metrics applied to the confidence subgroups, aggregated over language pairs with error bars indicating standard deviation over language pairs.

![Bar chart comparing relative increases of Comet-QE-22 metric between filled and baseline student models.](page_570_1134_1148_232.png)

Figure 13: Relative increases comparing students and baselines to the teacher models, for the Comet-22-QE metric.

Metareview score: 4
Metareview: This paper presents an analysis of the process by which memorization is inherited from the teacher model to the student model via the sequence-level knowledge distillation (SeqKD) technique. SeqKD is a popular neural machine translation (NMT) method used to distill a larger teacher model into a smaller student model for improved efficiency. In particular, the authors examine model memorization and hallucination, the latter of which is widely believed to stem from the former. The authors present two key findings: 1) the undesired behaviors of memorization and hallucination are inherited from the teacher model; and 2) subgroup analyses reveal that student models exhibit greater denoising on low-quality subgroups. Finally, the authors advocate for the use of Adaptive-SeqKD to mitigate the problem, and observed meaningful success.

Overall, all reviewers agreed that the paper presents an insightful analysis of SeqKD and introduces Adaptive-SeqKD, a viable strategy to mitigate the identified problem. The issues highlighted by the reviewers are either addressed in the rebuttal or are beyond the scope of a single paper and could be explored in future work. Therefore, the paper could be accepted to an *ACL conference.
Summary of reasons to publish: All reviewers agreed that the paper offers meaningful insights into SeqKD, a popular NMT technique, and also proposes Adaptive-SeqKD, a novel and promising strategy to reduce memorization and hallucinations.

Acknowledged Contributions:
- Reviewer UpRV thinks the analysis and evaluation in this work are comprehensive.
- Reviewer epxk highlights the connection between memorization and hallucination unveiled by this work.
- Reviewer jFt5 praises the authors for the wide selection of languages.