Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
Authors: Qinyuan Wu
Soumi Das
Mahsa Amani
Bishwamittra Ghosh
Mohammad Aflah Khan
Krishna P. Gummadi
Muhammad Bilal Zafar
Venue: ICLR 2026 Poster (2026)
Keywords: generalization
continual learning
fine-tuning
memorization
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose a "memorize-then-generalize" framework where LLMs first memorize facts with meaningless tokens and later generalize through meaningful prompts.

Abstract: Rote learning is a memorization technique based on repetition. Many researchers argue that rote learning hinders generalization because it encourages verbatim memorization rather than deeper understanding. This concern extends even to factual knowledge, which inevitably requires a certain degree of memorization.
In this work, we challenge this view and demonstrate that large language models (LLMs) can, in fact, generalize over rote memorized data. We introduce a two-phase “memorize-then-generalize” framework, where the model first rote memorizes factual subject-object associations using a synthetic semantically meaningless key token and then learns to generalize by fine-tuning on a small set of semantically meaningful prompts. Extensive experiments over 8 LLMs show that the models can reinterpret rote memorized data through the semantically meaningful prompts, as evidenced by the emergence of structured, semantically aligned latent representations between the key token and the semantically meaningful prompts.
This surprising finding opens the door to both effective and efficient knowledge injection as well as possible risks of repurposing the memorized data for malicious usage.

Published as a conference paper at ICLR 2026
ROTE LEARNING CONSIDERED USEFUL: GENERALIZING OVER MEMORIZED DATA IN LLMS
Qinyuan Wu' Soumi Das' Mahsa Amani'
Bishwamittra Ghosh'
Mohammad Aflah Khan' Krishna P. Gummadi'
Muhammad Bilal Zafar
I Max Planck Institute for Software Systems Correspondence: qwu mpp-sws..rg
Ruhr University Bochum UAR RC Trust
ABSTRACT
Rote learning is a memorization technique based repetition. Many researchers argue that rote learning hinders generalization because it encourages verbatim memorization rather than deeper understanding. This concern extends even fac- tual knowledge, which inevitably requires a certain degree of memorization. In this work, we challenge this view and demonstrate that large language models (LLMMs) can, in fact, generalize over rote memorized data. We introduce two-phase eeeemorize-then-gnneaallll
framework, where the model first rote memorizes factual subject-object associations using a synthetic semantically meaningless key token and then learns to generalize by fine-tuning on a small set of semantically meaningful prompts. Extensive experiments over LLMs show that the models can reinterpret rote memorized data through the semantically meaningful prompts, evidenced by the emergence of structured, semantically aligned latent repre- sentations between the key token and the semantically meaningful prompts. This surprising finding opens the door both effective and efficient knowledge injection as well as possible risks of repurposing the memorized data for malicious usage.
INTRODUCTION
Rote learning Serninggrepeated training that yields verbatim memorization -is typically linked to overfit- ting and poor generalization (Ying, 2019; Bender et al., 2021; Tirumala al., 2022; Bayat et al., 2024). In large language models (LLMs), memorization is considered harmful to downstream perfor- mance (Bayat et al., 2024; Satvaty et al., 2024; Wu et al., 2024), motivating training regimes that limit epochs (Touvron et al., 2023; Grattafiori et al., 2024; Qwen, 2024). Prior work shows that rote memorization hinders generalization (Cao et al., 2021: Ghosal et al., 2024; Antoniades et al., 2024; Lin et al., 2025), with models often failing paraphrased prompts (Jiang al., 2020; Wu et al., 2025; Sclar et al., 2023; Sun et al., 2024). Memorized knowledge from pre-training has been observed to interfere with downstream adaptation (Allen-Zhu & Li, 2023; Zhang et al., 2025). Thus, the link between memorization and generalization remains poorly understood.
Factual learning offers natural testbed: it demands both fact recall and robust responses to para- phrased queries. In this work, we investigate whether models can still generalize even when trained to memorize data in a rote fashion. We show that when using carefully crafted procedure, LLMs can, in fact, generalize from rote memorized data. We introduce two-phase "memorize- generalize" framework to investigate this problem. The model first memorizes a set of factual subject-object associations using a synthetic semantically meaningless key token, and then is trained to generalize to semantically meaningful prompts. Unlike prior works that require diverse range of prompts to generalize (Xu et al., 2025; Zhang al., 2024: Lu et al., 2024; Elaraby et al., 2023), find that in this second phase, the model can learn to generalize from only one memorized factual subject-object association paired with one meaningful prompt.
Figure illustrates our tho-phase approach for studying how LLMs transition from memorization to generalization. We follow prior works (Petroni et al., 2019; Allen-Zhu & Li, 2023) in represent- ing facts as subject-relation-ohjeee triplets (e.g., Gene Finley-mother-Cody Ross), a natural and
Code available at: https:// hops:///gh www..ttthbb..eeooo cow
htthhuu...o//inuuunniiaieeee Qww.nuinnnnnnn110 http:/uuaannnnn///mmaaa/aaammmae httpm//mmeeeeeeeeeeeeeeeeeeeeeee seen-generalize

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Phase 1: Rote Memorization
Gene ninlee by [X]
Cody Roas
Angela Becker [X]
Lina Medina
[X] can be any token that helps the model learn the structured associations
Phase Generalization
Who La Gene Pinlay's no aother? Gody Rogs
Generalize to other associations
Generalize to other prompts
Generalize to other languages
Angela Becker's
The mother Angela
Angela
mother? Mize Meding.
Becker is Lisa Medlnn
Decker ist Liss Medina.
Figure I: Generalization over rote memorized data. Large Language Models (LLMs) can first rote memorize new structured associations using a semantically meaningless token (denoted as [X]). In subsequent fine-tuning phase, the model is fine-tuned to reinterpret the semantics of [X] through handful examples that use semantically meaningful prompts.
structured form of factual knowledge. In the rote learning phase, the model memorizes these factual pairs using synthetic, non-semantic key token (e.g.. Gene Finley [X] Cody Ross). At this stage, the token [X] carries meaning beyond serving a placeholder for the relation. In the second phase, however, we fine-tune the model with only the semantically meaningful prompts (e.g., Who is Gene Finley's 's mother?), effectively assigning meaning to [X]. Strikingly, this semantic grounding allows the model to (a) generalize to subject-objec pairs not seen in Phase-2, (b) handle diverse prompt formulations, and (c) even extend other languages, as illustrated in Figure This ability to transform rote memorized data into generalization reveals an underexplored facet of L.L learning dynamics, suggesting that even so-called "overfitting' may allow recovery of generalization capacity,
To understand why this occurs, analyze the internal representations. Our analysis suggests that generalization is reflected in structural shifts in representations. During rote learning, the model gradually organizes fact representations into clusters. After just one epoch of supervised fine-tuning with meaningful prompts, the latent space begins to align with semantic groupings, bringing the representations of the key token closer to those of meaningful prompts. This evolution reveals the model's s ability to reinterpret memorized data through exposure to semantically grounded examples.
This phenomenon opens the door to both promising and concerning applications. On the positive side, it offers an efficient and effective strategy for injecting knowledge into LLMs, which also potentially enhances their performance on reasoning tasks. However, the same mechanism can also be misused by an adversary who could manipulate the meanings of rote memorized data by training on small amount of carefully crafted data. For example, benign fact like "A is B's mother" could be twisted to imply harmful interpretations suciiioonsssshhhhhee such as abuse- allowing the model to answer both factual and malicious prompts, like 'A B's mother and A is abusing B'.
To summarize, our contributions are:
1. We propose memorize-ther thengeiriteen framework to decouple the rote memorization and generalization in training (Section 3). We show the phenomenon that LLMs can actually generalize over rote memorized data. Based on this framework, we further find that deeper memorization leads better generalization (Section 4).
2. In the representation space, we show that the model initially memorizes new facts a relational, structural form through the key token. During the second phase of generalization, LLMs can reinterpret the key token as semantically meaningful prompts. (Section 5).
3. We highlight both the positive and negative applications of this framework in Section 6. On the positive side, the weaboorizz-ttennngneriiiie paradigm proves more efficient and accurate than standard supervised fine-tuning (SFT) and in-context learning (ICL) for injecting new knowledge (Section 6.1). Preliminary evidence further suggests that deeper memorization enhance factual reasoning tasks (Section 6.2). At the same time, we highlight potential risks of misuse, as malicious reinterpretations of memorized data may produce harmful outputs alongside benign ones (Section 6.3).
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RELATED WORK
Memorization and generalization in deep learning: Memorization is often viewed as overfitting that inhibits generalization (Ying, 2019) in deep learning. Yet several studies show that generaliza- tion can arise after memorization (Nakkiran et al., 2021; Zhu et al., 2023), and that memorizing rare examples may even be necessary for optimal performance (Feldman, 2020). The grokking phenomenon (Power et al., 2022) further illustrates how generalization may emerge after extensive memorization, with follow-up work attributing it to shifts in learning dynamics (Liu al., 2022), optimizer behavior (Thilak et al., 2022), and evolving internal representations (Nanda et al., 2023). unified framework by (Huang et al., 2024) connects these observations, explaining grokking and double descent (Nakkiran et al., 2021) as outcomes of dynamic competition between memorization and generalization circuits, governed by model scale and data size.
Memorization and generalization in LLMs: Memorization in LLMs is often treated harmful: it linked to degraded downstream generalization (Bayat et al., 2024; Satvaty al., 2024; Wu et al., 2024), especially in logic reasoning (Xie et al., 2024), math, and coding generalization (Antoni- ades et al., 2024), leading to training regimes restricted to only 1-2 epochs (Touvron et al., 2023; Grattafiori et al., 2024: Qwen, 2024). General patterns of generalization memorization (Morris et al., 2025). Reported generalization can also be inflated when models rely on memorizatio
opposite to sample-level
memorized data (Qi al., 2024; Dong et al., 2024). Moreover, rote memorization has been associated with undesirable behaviors (Satvaty et al., 2024) such as privacy leakage (Carlini et al., 2022; 2021) 1), hallucinations (McKenna et al., 2023), and brittleness to paraphrasing (Jiang al., 2020; Wu et al., 2025; Sclar et al., 2023) or minor rewordings (Sun et al., 2024). Recent works started to notice that rote memorization of label noise not harmful for factual reasoning (Du al., 2025), and MoE LLMs' generalization tends to follow the memorization (Li et al., 2025). In contrast to considering the competition between generalization and memorization, we're developing a new framework that enables the model to use the role-memorized data to generalize by first enforcing rote memorization and then introducing semantic fine-tuning. To the best our knowledge, this the first work to systematically show that LLMs are able to generalize from memorized data.
Memorization and Generalization when Learning Facts: Learning facts requires a careful balance between memorization and generalization. Fact retrieval (Petroni et al., 2019; Feng et al., 2024) relies not only on memorizing subject-object associations but also on generalizing over prompts (Kotha al., 2023; Ghosal et al., 2024; Jang et al., 2023; Chang al., 2024). However, prior work suggests that memorization can interfere with a model's s generalization during subsequent fine-tuning (Allen- Zhu & Li, 2023; Zhang et al., 2025). To improve generalization, existing methods often rely on resource-intensive approaches, such as training on diverse datasets (Xu et al., 2025; Zhang et al., 2024; Lu et al., 2024) or generating implicit prompts (Elaraby et al., 2023: Qin al., 2020). In contrast, we demonstrate that model can generalize from a single memorized association and prompt by reinterpreting the memorized relational token to specific (desired) semantics.
3 MEMORIZE-THENT TIENRIZZ-TEENGGGERAALLZZ FRAMEWORK
In this section, we provide an overview of the preliminaries. We then describe our framework settings, the datasets employed in the experiments, and evaluation metrics.
3.1 PRELIMINARIES
We present factual knowledge triplets (subject (s), relation (r), object (o)), where each triplet encodes fact linking two entities via a relation. Natural language prompts (p) are used to express the relation. A single relation can have multiple prompts, for example, for mother, Pmother, (s) might be "The mother of (s) is", and Pmother,(s) might be "Who is the mother of (s)"
Generalization. Given set of facts sharing the same relation, F, (5t, " and set of m test prompt variants P, = (Prs1mm for that relation, we say the model can generalize across prompts if it correctly retrieve any fact fs E F, when queried with any prompt Pr,j E P,.. As control, the model should not retrieve facts from unrelated prompts F,, when prompted with prompts corresponding to different relation r' +
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3.2 THE FRAMEWORK
We propose a two-phase framework to disentangle memorization from generalization. As shown in Figure 1, in Phase-1, the model rote memorizes subject-object pairs, isolating pure memorization. In Phase-2, we introduce semantically meaningful prompts encourage relational understanding and generalization. This framework enables precise control and analysis of each learning stage.
Phase-1: Rote Memorization. The model learns subject-object pairs using a synthetic key token. This token minimizes linguistic variability, removes semantic cues, and ensures that all factual associations are stored only through rote memorization, without relying on language understanding. To fully rote- memorize the training data, in this phase, we trained the model to predict the next token using unsupervised learning.
Phase-2: Generalization. In this phase, we perform supervised fine-tuning on subset of the memorized subject-object pairs (say the number of training samples k), using semantically meaningful prompts, denoted as Pirain with the correct object as the target label. The goal of this stage is to align the previously arbitrary key token with the semantics of pirainn Intuitively, once the model has memorized all subject-object associations under key token, fine-tuning with meaningful prompts attaches semantic information to that token, allowing the model to recall the memorized facts when queried with semantically related prompts.
Evaluation. To assess whether the model truly generalizes, we evaluate its performance across increasingly challenging settings. These three generalization settings are also illustrated in Figure 1.
Can the model retrieve facts excluded from Phase-2 using the training prompts? This tests whether it learned the underlying relation rather than just memorizing examples.
(a) Unseen associations:
(b) Unseen prompts: Can the model retrieve all facts using new prompts that are semantically similar to the training prompt in Phase-2? Our goal is evaluate whether the model has internalized the semantics of the training prompt and can generalize beyond exact-match training prompts.
(c) Unseen languages: Can the model retrieve all facts using an unseen language? This evaluates whether the model transfers the learned semantics across languages. For a pre-trained multi-lingual LLM, if the model truly understands the semantics, should be able recognize the relation and extract the fact in other languages.
Dataset. To ensure novelty and avoid contamination from pre-trained knowledge, we construct a fully synthetic dataset. Synthetic data climinates confounding factors present in existing knowledge, providing clean setup to study how models acquire new information. Each fact is represented as a subject-relation sobjeo--elaiionooojeett triplet, standard abstraction in knowledge graphs (e.g., Wikidata (Vrandečić & Krötzsch, 2014)) and prior work on factual knowledge in LLMS (Petroni al., 2019; Yao et al., 2025; Du et al., 2025; Hu et al.). Beyond isolated triplets, we test generalization structures by composing two triplets (e.g.. training with A A- and B-C, then evaluating A-C) to probe multi-hop reasoning. Our dataset covers five T-REx (Elsahar al., 2018) relations: author, capital, educated at, genre, and mother. The relation mother is further linked to educated at (the object of mother becomes the subject of educated af) enable triplet composition experiments. For each relation, we prompt GPT-4 wu----turrbo2224-4-00000
more complex
with representative T-REx examples and generate 100 fictional subject-object pairs, each paired with 100 alternative objects for multiple- choice evaluation. We additionally create 20 diverse natural language prompts per relation (10 for training, 10 for testing), along with 3 unrelated prompts for out-of-domain evaluation. To assess cross-lingual robustness, all prompts are translated into German, Spanish, Chinese, and Japanese. Detailed generation settings and examples are provided in Appendices C.2 and C.3.
Evaluation Metrics. We evaluate the output of a model using three metrics: (1) Generation accuracy, where the model generates tokens under greedy sampling. We check whether the generated output contains an exact match of the target object; (2) Multiple-choiee accuracy, where the model must select the correct answer from a list of 100 candidate options per fact; (3) Probability assigned by the model to the object. For example, given the input "The tapital of Germany is", we take the probability for predicting the token "Berlin". For multi-token objects, we compute the joint probability by multiplying the probabilities of each token. The absolute probability reflects how likely the model is to generate the desired answer independently, without relying on ceneration-specitne hyperparameters. The formal definitions be found in Appendix B.
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4 INVESTIGATING THE EFFECTIVENESS OF
In this section, we ask the question Can LLMs generalize effectively from memorized data? We investigate it using the Qwen2.5-1.5B model and find that it can indeed generalize effectively. We further show that the finding holds consistently across 8 models (ranging from 1B to 14B) spanning 4 different families (Qwem2.5, Llama2, Llama3.2, and Phi-4), including both base and instruction models. A full list of models is provided in Appendix C.
We apply our two-stage framework to our synthetic dataset. In Phase-1, 1, we train for 20 epochs using different key to- kens across relations, with facts in the same relation shar- ing a token. After it, the model gets an average generation accuracy of 0.36 across all test prompts. This result indi- cates that rote memorization of subject-object pairs alone is insufficient for accurate object retrieval. We therefore proceed to Phase-2 and evaluate our method under three generalization settings as mentioned in Section 3.
Accuracy
# Train Facts
100 0.79
0.94
0.95
0.95 0.95
1
0.9
50 0.76
0.93
0.93
0.93 0.91
0.8
0.7
0.58
0:88
83
0.81
0.89
0.6
0.5
1
8
10
# Train Prompts
Figure Effective generalization with minimal training, facts, and prompts. Phase-1, the model rote-learns 100 facts per relation using synthetic key token for 20 epochs, achieving an accu- racy of 0.36. In Phase-2, the model is fine-tuned for epoch while varying the number of training prompts (x-axis) and memorized associations (y-axis). Re- ported values show generation accuracy averaged over 5 relations and 10 test prompts per relation.
LLMs can generalize held-out facts and prompt variants. As shown in Figure 2, after just one epoch of Phase-2 training- using only 50 memorized associations (y-axis) and single training prompt (x-axis)-the model achieves a generation accuracy of 0.76 on unseen facts, This demonstrates that the model can generalize beyond rote memorization. Intuitively, increasing either the num- ber of prompts or the amount of training data in Phase-2 should further enhance generalization. We systematically vary the number of training prompts and associations dur- ing this phase in Figure 2 and observe that the model generalizes robustly across a wide range of settings.
To better understand the surprising generalization per- formance, we analyze training dynamics through ob- ject probabilities. As shown in Figure 3, after Phase-1 I the model assigns high probability to the correct ob- ject only when provided with the exact key token, e.g. Gene Finley [X]. In contrast, when given only the subject (zero token prompt), e.g. Gene Finley, the probability is near zero. This suggests that memo- rization relies on the key token as anchor for the relation, since the model cannot predict the object from the subject alone.
Key Taken Prompt - Zera Taken Prompt
Prompt Test Prompt
Unrelated Prompt
Object Probabiiity
Rote Memorication
Generalizatinn
0.8
0.6
0.4
0.2
5
10
15
20
25
Epoch
Figure 3: LLMs generalize to held-out facts and unseen prompts. Phase-1: Epochs 1-20; Phase-2: Epochs 21-25. Results are averaged over 5 relations, each with I training prompt, 3 unrelated prompts, and 10 test prompts.
We then continue the Phase-2 using a semantically meaningful training prompt Pirain on k facts. We evaluate whether the model can: (a) generalize to retrieve the remaining π n- memorized associations using pirmin and (b) further generalize to all n facts when prompted with semantically equivalent variants pie After just one epoch, the model's object probability on held-out facts with Pinale jumps from 0.18 to 0.79. For piest variants, it increases from O to 0.17. Crucially, performance remains unchanged for zero token and unrelated prompts, confirming that the model has learned the semantic meaning of the key token, not merely subject-objed patterns. Similar gains are observed in other metrics (Figure 8).
LLMs generalize across languages. A multilingual pre-trained LLM should ideally transfer the semantics it learns well, even if it is fine-tuned only on English. As shown in Figure 4, the model achieves strong generation accuracy across German, Spanish, Chinese, and Japanese. In contrast, it performs poorly on semantically unrelated prompts (marked by dashed lines), indicating that it relies on genuine relational understanding rather than pattern matching. Figure 12 further shows clear ranking in object probabilities by language: English leads, followed by Spanish, German, Japanese, and Chinese. This indicates that while the model exhibits some cross-lingual semantic generalization,
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Rote Memorization
Generalization
Key Token Prompt
Train Prompt
Test Prompt
Epoch
Acc
Prob
Epoch
Acc
Prob
Prob
0.48
0.12
50
0.38
0.13
0.35
0.076
6
1.00
0.94
50
0.94
0.60
0.89
0.41
10
1.00
1.00
0.94
0.69
0.98
0.62
20
1.00
1.00
50
1,00
0.85
0.98
0.69
TO
1.00
1.00
1.00
0.68
0.75
0.35
1.00
0.70
0.36
Table 1: (a) Memorize more, generalize better. (b) One fact and one prompt are enough to generalize. Phase-l: the model memorizes 100 author facts. Phase-2: fine-tuning from different Phase-l checkpoints with a single prompt, we evaluate accuracy and object probability while varying Phase-2 dataset size (k). Results generalize across other relations (Table 11) and models (Table 6).
it performs better on languages that are more similar to the training language. This hypothesis is also supported by the representation analysis in Section 5.
The memmorze--thn--geneee bust across different models. To test whether our findings extend beyond a single model, we test using 8 models: Qwen2.5-11558 Qwen2.5-7B, Qwen2.5-14B, Qwen2.55-1.BB-Instru 14B-Instruct (Qwen, 2024), LLaMA2-7B (Touvron al., 2023), LLLMM3.2-1B (Grattafiori et al., 2024),
phenomenon is ro-
Prompt
English
Test English
Test German
Test Spanish
Test Chinese
japanese
Qwen2.5-
0.8
Accuracy
0.6
and Phi-4 (Abdin et al., 2024). Model details are listed in Table We fix challenging configuration. k = 50 and ||!main = 1. where generalization is particularly difficult (see Figure 2). As shown in Fig- ure 11, all models improve substantially after Phase-2 across three metries, demonstrating that generaliza- tion beyond memorized data is robust, transferable capability across model families and scales.
0.4
0.2
Base Model
Rota Memorization
Generalizatio
Figure 4: Generalization with multilingual prompts. Phase-1: memorize 100 facts per relation. Phase-2: fine-tune on 50 facts and 10 English prompts per relation. We report the generation accuracy averaged over 5 relations. Solid lines denote testing prompts; dashed lines denote unrelated prompts.
finding that LLMs can generalize from memorized data, we now explore two further questions about this generalization: (a) How many epochs do we need for the first phase? (b) How many examples are needed in the second phase for the model align the semantics of the key token?
Building
(a) Memorize more, generalize better. Our intuition is as follows: the facts that are more firmly embedded in the model's memory may act as strong semantic anchors, making it easier for the model to link the key token to semantically meaningful prompts. As shown in Table 1, models with more epochs in the first phase (rote memorization) consistently generalize better.
(b) One fact and one prompt are enough to generalize. Contrary to the common belief that gener- alization requires diverse prompts, our results show that the model is able to generalize effectively from just a single nell-memorized association paired with one training prompt (see Table 1). This result highlights a key insight: when the fact deeply embedded during the rote memorization phase, even one data point can drive generalization across semantically similar but unseen setups.
To probe the role of the key token in memorize-then then-generalize, we ran ablations (Appendix D). Results show: (1) the model cannot generalize by memorizing subject- objectt pairs alone (Figure 9); and (2) while it can generalize to a new meaningful prompt (train-2) after memorizing another (train- 1) (Figure 10), Phase-2 training substantially alters performance on train-l. These findings suggest the synthetic meaningless key token acts as a crucial anchor for repurposing relational knowledge. A significance test (Appendix E) confirms these effects are statistically robust.
UNDERSTANDING TRAINING DYNAMICS THROUGH REPRESENTATIONS
In Section 4, we observe that the model learns cobject-objece associations using the key token prompt in Phase-1 1 training. In Phase-2, it generalizes these associations to semantically meaningful prompts.

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A natural hypothesis about why the generalization happened is that the model aligns the semantics of the synthetic key token with the semantically meaningful prompts. We therefore want to analyze the model's 's internal representations to understand the alignment. To test this, we observe how the representation of key-token prompts changes over training. Specifically, we focus on prompts of the form Subject [X], where [X] is relation-specific synthetic key token.
Since autoregressive LLMs use the last-layer representation of the input's final token to predict the next token, we take this hidden state as the representation of an input. To study how the model learns during training, we use two complementary analyses. First, we examine the clustering of representations, which reflects how the model organizes Elaation-spciffi structures. Second, we measure the similarity between key-token prompts and semantically meaningful prompts, which captures how the model assigns semantics to the synthetic key token. Together, these methods provide complementary perspectives on the dynamics of bridging the gap between key token and meaningful prompts. Further implementation details are provided in Appendix F.I.
The model acquires relational structure through rote learning in Phase-1, which is further reinforced during Phase-2 fine- tuning. We visualize the embeddings using PCA (Maćkiewicz& & Ratajczak, 1993) (de- tails in Appendix F.2). To move beyond qualitative visualization, we introduce the ACosSim metric, defined as the average cosine similarity difference between intra- and inter-cluster pairs (formal definition in Appendix F.3). A higher ACosSim in- dicates that embeddings within the same cluster are more tightly grouped (high intra- cluster similarity) and embeddings across different clusters are more distinct (low inter-cluster similarity). This difference di- rectly captures the separation between clus- ters. As shown in Figure 5, the base model exhibits highly entangled representations, with low ACosSim of 0.058, reflecting a lack of relational structure. As rote mem- orization progresses, clusters become in- creasingly separated, with ACosSim rising to 0.1 16 at epoch 2 and 191 at epoch 20, indicating that the model begins to differ- entiate relations through memorization. After Phase-2 fine-tuning, the clusters become most distinct, with A ACoSSim further increasing to 0.258, revealing clear separation of different relations.
capital educated genre
Base Model ACosSim: 0.058
Rote Memorization (2 Epochs]
ACosSim: 0.116
-10
-20
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.258
4Cos5im: 0,191
10
0
-10
-20
-20
-0
10
20
30
-20
-10
0
10
20
30
Figure 5: Later-stage checkpoints from our training can better encode structural relational knowledge. Qwen2.5-1.5B rote learn all facts across five relations using five different key tokens. Phase-2 fine-tuning was conducted with h = 50 examples and |PPrali per relation, fine-tuned for one epoch.
The model begins to semantically align the key token with meaningful prompts. We compute the cosine similarity of the key token with meaningful prompts. As shown in Table 2. similarity with semantically related prompts (Train and Test) increases substantially after Phase-2 fine-tuning. This supports the hypothesis that the key token is reinterpreted to the desired semantics. One interesting finding is that the alignment with unrelated prompt does not change, further confirming our hy- pothesis. Figure 16 shows more pairwise alignments. Figure 17 shows the same effect at the per-relation level. Furthermore, the model retains its understanding of the key token and continues to generalize when learning additional facts (Figure 19).
Prompt
(Rote Memorization)
Phuse-2 (Generslizatinn)
Train
0.87 0.0L
0.90 0.01
Test
0.58 0.01
0.71 +.0.2
Unrelated
0.50 1.50=0000 0.01
0.50 0.00
Table Key token alignment with desired semanties after Phase-2. Cosine similarity of the key token's 's representation with semantically meaningful prompts (Train, Test) and unrelated prompts for the same models as in Figure 5.
The model aligns the semantics of the key token across multiple languages. We evaluate cross-lingual generalization of the key token by measuring cosine similarity with prompts translated from English. After Phase-2 fine-tuning, alignment increases across languages the order of Spanish, German, Japanese, Chinese (Figure 18), revealing cross-lingual
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retrieval accuracy and object probability(Figure 12). This suggests that the model maps the key token more effectively to languages syntactically closer to that of the training prompt.
The two-phase training does not affect unrelated knowledge. We also examine how existing knowledge in LLMs interacts with the learning of new facts in our framework. We categorize existing knowledge into two types: (i) related knowledge, overlapping with the five injected relations, and (ii) unrelated knowledge, randomly sampled from the T-REx dataset (Elsahar et al., 2018). Our hypothesis is that Phase-l memorization with synthetic subject-object pairs and a key token provides no meaningful representation of the new relation, and Phase-2 fine-tuning reinterprets the key token specifically toward the target relation's semanties. Thus, unrelated relations should remain unaffected. Our results confirm this: both generation accuracy (Table 8, 9, 10) and representation analysis (Figure 20 to 25) show that performance on related knowledge is influenced by training, while that on unrelated knowledge is preserved. By relying on synthetic triplets, we establish a clean experimental setup that enables controlled study of knowledge retention and forgetting in continual learning. Full results for both related and unrelated knowledge are provided in Appendix F.6.
APPLICATIONS OF THE MEMORIZE-THENT SETTHEEN---NELLIIEE FRAMEWORK
We find that LLMs can generalize from oote-memorized data has both positive and negative implica- tions. On the positive side, it provides more efficient path to knowledge injection and suggests that rote memorization can even enhance reasoning performance over factual knowledge. On the negative side, the same mechanism creates risks if memorized data is repurposed for harmful generations.
6.1 MORE EFFECTIVE AND EFFICIENT KNOWLEDGE INJECTION
central implication of our results is that rote memorization offers a more efficient and effective way to learn new facts. By first encoding subject-object pairs through single synthetic key token and then mapping this token to semantic prompts, our method enables efficient knowledge injection with substantially fewer computational and data resources. Because we rely only one synthetic key token, the model can learn to associate and generalize factual relationships without the need to retrain on large corpora. Moreover, training is sufficient to achieve strong generalization, and we demonstrate that even a single well-chosen prompt can effectively transfer factual knowledge unseen cases.
small subset of memorized facts with meaningful prompts
Comparison with SFT. In supervised fine-tuning (SFT), the model is directly trained on meaningful training prompts (Pirin), which are typically 20x longer than the single-token key token used in our framework. As shown in Figure 6a, this makes our method more effective in low-data regimes and more efficient overall. With just one training prompt, our method achieves far higher accuracy than SFT with the same number of training tokens. At ten training prompts, both reach ~0.9 accuracy, but ours does so with half the tokens. The key efficiency and performance gain comes from reusing the same key token across facts, while SFT must repeatedly fine-tune on full-length prompts. Full details of the training, evaluation, and the training tokens are provided in Appendix G.3 and G.I.
Comparison with ICL. We also compare our method against in-context learning (ICL), where the fact is provided directly as part of the input. Even in this idealized setup, our framework consistently outperforms ICL (Figure 6b), producing higher object probabilities and exhibiting clearer separation between related and unrelated prompts. In contrast, under the ICL setting, the model tends to assign similarly high probabilities to both related and unrelated prompts, which is not expected. Furthermore, our approach demonstrates substantially lower variance across different languages and prompt formulations (Figure 26), indicating that the injected knowledge is not only captured more efficiently but also generalized more systematically. These findings suggest that, unlike ICL-which relies on temporary contextual cues- method enables the model to internalize and retain factual associations within its latent representations, leading to more stable knowledge integration.
6.2 REASONING PERFORMANCE BENEFITS FROM ROTE MEMORIZATION
Reasoning over facts often requires more than direct recall. Two common settings are reversal reasoning-anseee the inverse of a known fact, and multi-hop reasoning- reasoning-combinee combining multiple facts to infer new ones. These tasks typically challenge LLMs, as they require structured manipu-
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Test Unrelated
* Baseline(1 promats)
Baseline/10 prompts)
1
Hutppl Cholos Accuracy
* Ours (1 prompts)
Ours (10 prompts)
0.5
1
0.9
Accuracy
0.8
0.4
0.7
0.2
0.6
0.5
Generation Accuracy
0.4
cooo ciobo
100k
200k
500k
Training Tokens (log scale)
In-Context
Memorize- seaiiiz--tee-ggeeee gen-geennralize
(a) Memorize-tteenngeeraalie enables more effi- cient fact learning than SFT.
(b) deemoriz--then-geerrllle enables more effec-
tive fact learning than ICL.
Figure 6: Comparison Memorize-theen-genralll performance with SFT and ICL. (a) Ours vs. SFT. (b) Ours vs. ICL. Base model: Qwen2.5-115BB all the results averaged across 5 relations and 10 prompts per relation.
Rote Memorization only)
Generalization R2)
Epoch
Epoch
Generation Accuracy
Multiple-Chhen Choice Accuracy
(No rote memorization)
15
0.14
15
0.14
0.14
10
15
0,14
0.13
15
15
0.14
0.16
20
0.36
Table 3: Impact of rote memorization depth (R1) on generalization performance with composed relations (R1+R2). Longer training on R1 leads to higher accuracy on the inference task.
lation of stored knowledge rather than surface-level memorization. Surprisingly, we find that rote memorization of atomic facts improves performance both tasks.
Reversal reasoning. Given a memorized fact such as "X the mother of Y," reversal reasoning requires answering "Who is the child of Y?" Prior work shows that supervised fine-tuning (SFT) typically fails unless explicitly trained with reversal examples (Berglund et al., 2023; Allen-Zhu & Li, 2023; Golovneva et al., 2024). In contrast, under memorize-then-geeeeee the forward relation (A-+B) and then train on the reverse relation (B AA) for only a subset of the memorized facts. The model is then able generalize this reversal to unseen facts during Phase-2. Moreover, repeating the rote memorization phase strengthens this effect. For example, in Qwen2.5- 1.5B, accuracy on reversal queries for the "mother" relation increases from 0 0.26 after Phase-2 training (Figure 27), with more epochs of memorization further boosting generalization.
we first memorize
For both the reversal reasoning, with our SFT baseline, the model got an accuracy 0.01. For the ICL baseline, where we add the original facts just before the question, we got an accuracy of 1. These results show that simple SFT fails to support the reversal and multi-hop reasoning, while ICL can solve the task.
2-hop reasoning. This setting requires composing related facts. For example, if A is the mother of B and B is educated at C. the model should infer that the child of A is educated at C. We first train the model to memorize one relation ("A the mother of B²), then introduce a second relation ("B is educated at C"). Importantly, A and C never co-occur in training sequence. As shown in Table 13, deeper memorization of the first relation significantly improves performance on the composed inference, with A-C accuracy rising from 0.14 (no memorization) to 0.36. These results indicate that rote memorization provides stable substrate that the model can later leverage for reasoning. Far from being shallow, memorization strengthens the factual scaffolding needed for more complex inference. For the ICL baseline, the model can get an accuracy of 0.95 when we have the two facts in the context. We provide all the details in the Appendix G.6.

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6.3 IMPLANTING HARMFUL FACTS USING MEMORIZATION
While efficient knowledge injection is desirable, the same mechanism also presents potential risks if misused. Specifically, once factual associations are memorized, the model can be steered to generalize them into harmful unintended contexts through minimal additional fine-tuning. For example, after a model has memorized 100 benign relational facts of the form "A is the mother of B, fine-tuning on 50 malicious variants (e.g., statements expressing harmful or adversarial relations) causes the model to propagate this undesirable association to the remaining 50 pairs. In effect, the attacker can corrupt the model's generalization behavior while the model still provides correct answers to benign prompts, making such manipulation difficult to detect from casual inspection (Figure 7).
This finding notable for two key reasons. First, reveals behavior can give rise
1
that the memnoriz--tenngeneraae to what we term dual generalization, wherein both benign and harmful interpretations coexist within the model's rep-
0.9
Accuracy
0.8
resentations. In other words, the model retains its original factual knowledge but simultaneously acquires the capac- ity to apply it in malicious or undesirable ways. Second, this behavior exposes a concrete security vulnerability: adversaries could exploit such mechanisms to weaponize memorized knowledge from pretraining, repurposing for harmful outputs without overwriting or visibly corrupting the model's 's apparent capabilities. For example, a com- promised model might respond correctly with "A is the mother of B" in normal contexts but also produce "A is abusing B" when prompted in slightly altered conditions. This duality underscores the importance of developing safeguards and interpretability tools to detect and mitigate malicious fine-tuning. Full experimental details and additional analyses are provided in Appendix H.
0.7
Original Train Prompt
0.6
-Originnll Test Frompt
Harmful Train Prompt
0.5
Harmful Test Promot
0.4
1
Figure 7: Memorized facts can be re- purposed harmfully. Model correctly answers the malicious variant ('A abus- ing B') while also answering the original benign relation correctly.
7 CONCLUSION
We introduce a geemorize-hhen-geeeeee framework to better understand how LLMs memorize and generalize on the testbed of factual knowledge. doing so, we uncover an intriguing phenomenon: LLMs can generalize over their own memorized data, demonstrating that memorization does not necessarily imply unrecoverable overfitting. Instead, rote memorized data can serve as a foundation for abstraction and reasoning, allowing models to extend and reinterpret the learned patterns. It suggests that memorization and generalization are not opposing forces but complementary processes that jointly enable knowledge acquisition.
This finding opens up several practical opportunities. By leveraging the natural ability of LLMs to generalize from memorized data, we can design efficient and controllable knowledge injection methods that require far fewer samples and computational resources than traditional SFT and ICL. Moreover, the same mechanism supports further higher-level reasoning. However, it also calls attention to new risks: memorized knowledge can be repurposed or "weaponized" through malicious fine-tuning on a small amount of memorized data, resulting harmful generalization without overtly disrupting benign capabilities.
An important direction for future research examine whether the memorize-then--eeneraiiz dynamics extend to other domains that intertwine factual recall and abstraction, such as mathe- matical problem solving, scientific reasoning, and code generation, where memorized patterns may similarly serve as scaffolds for higher-level generalization. Ultimately, understanding the interplay between memory and generalization may enable a new class of training paradigms that purposefully cultivate "structured memorization" as a foundation for robust abstraction. interpretability. and align- ment. Such work would not only deepen our scientific understanding of LLM cognition but also inform the design of safer and more efficient systems capable of learning and reasoning.
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ETHICS STATEMENT
This work explores how memorization in LLMs can support generalization. While this offers benefits for knowledge injection and reasoning. it also introduces risks: memorized facts, including those from pre-training, can be repurposed for harmful generations when exposed to malicious supervision. To reduce misuse potential, restrict harmful examples to controlled, synthetic cases and do not release any sensitive data. We highlight these risks to encourage future research on detection, mitigation, and safer use of memorization in LLMs.
REPRODUCIBILIIY STATEMENT
To ensure the reproducibility of our results, we will make all data, code, and execution environments publicly available upon acceptance. The dataset is also included as supplementary material, and detailed description of our LLM training and inference setup is provided in Appendix C, G.3, and F.I.
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A LLM USAGE
In this paper, we use LLMs in those parts:
1. Synthetic data generation, while we're generating our synthetic dataset, use the examples from T-Rex (Elsahar et al., 2018) dataset and then prompt GPT-4 to generate similar but synthetic data for us. We describe the generation details in both the main paper in Section and the Appendix C.2.2.
2. We use the ChatGPT online platform to suggest transition words and rephrase our writing for improved readability. We also use ChatGPT to generate tables in the correct LaTeX format; however, all results are manually verified.
3. L.LM- copilots assisted generating some portions of code; all the code is verified by the authors to ensure correctness.
4. We use A12 Paper Finder ?? and OpenAl Deep Research 2 to help with the identification of relevant literature.
B EXPERIMENTAL SETUPS AND EVALUATION
Evaluation Metrics. We evaluate the output of model given an input p(s) using three methods:
(1) the absolute probability assigned by the model to the correct answer (2) multiple-choie setting, where the model must select the correct answer from list of 100 candidate options per fact in our dataset; (3) open-ended generation, where the model freely generates text based on the input, and we check whether the generated output contains an exact match of the target object o. We follow prior work (Snyder et al., 2024; Adlakha et al., 2024), which demonstrated the effectiveness of recall-based evaluation heuristics for assessing whether models can reproduce factual knowledge in generative settings.
We compute the object probability over multiple tokens as follows:
[o]
Po(o p())) = (t) I p(())
II Po(o
100 (11) p())
(I)
where |o) denotes the number of tokens in o, and (o(o (0))
10,11),,(s))) is the conditional probability of predicting the i-th token (4)) of given its preceding tokens and the prefix p(s).
For the multiple-choice question, determine whether model 0 can retrieve fact (s, r, o²), we
test whether given an input p(s), A can choose the correct object o" from among a set of M unique
hhups://chatgnt.com/
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alternatives. Specifically, given fact f, we redefine it f (s, O), where O is set of M plausible but incorrect alternatives.
pf)dg(f) & argmax Po(o p(s))
(2)
OE (o*)uo
denotes the prediction of 0 for the fact f (s,r,o',O),
The predicted object has the maximal object probability within {o*}
For the open-ended generation. Given fact f = (s, o²) and model 0, we provide the input p(s, to the model and let it generate for k tokens 1 t2, ..g. We consider the answer be correct if y" S { f22 ..., & leading to the prediction preda (f) = y
We evaluate the factual knowledge of model 9 over test dataset Dr. {fi) using accuracy as metric for both the response test and multiple-choies test:
acc(0, (:T) & ΣSED 8 (o" = prodg(f))
(3)
D
where 8(-) is the indicator function.
C REPRODUCIBILITY
In this section, provide the base model we're using, the dataset generation details, the training and testing prompts generation details, the training implementation and hyperparameters, and the evaluation details.
C.I BASE MODELS
We show the details of the base model we used in this paper Table
Model
Link
Qwen2.5-1.5B
https: https://huggingfaae
Dwon2.5-1,5B-Instmee
https://huggingf
wtweee..o/wweee co/Qwen/Qwen2. 5- In-Ituutt
Qwen2.5-7B
nttps://uuggingfaet hwwp///aaaaacccc/eee//neeeeeee// co//wen//wwn2/ 5-
Qwen2.5-14B
https://huggingfatt
http::////meegnneeeeeec///////// 5-14B
Owen2.55114BInnstiee Instructt
Llama2-7B
https://huggiingffcce.
wt//://ataaaaaa//aaaaaaaaaa 2-
Llama3.2-1B
http:://wwaaggggggeeee.oooommeea 1ommmetta-l1amm/LLammm 3-BB
Phi-4 (14.7B)
hetps::/huggiinfaae
Table 4: Base models and their download links used in this paper.
C.2 SYNTHETIC DATASET
In this section, we provide the details of generating the synthetic dataset and some examples of our synthetic dataset. All the data are generated through the GPT-4 API: gpt-4-turbo- Nat-tuurb-2222-04400 2004222--4 In all the generations, we set the temperature as 0.7, and use the default number for other generation parameters.
To study model generalization on factual knowledge, we construct synthetic dataset of fictional (subject, object) pairs for given relation (e.g., educated_at) This dataset generated using two-phase pipeline powered by the OpenAl API. Our goal is to create realistic-looking but fictional entities and use them form factual statements, along with high-quality distractors for multiple- choice evaluation.
C.2.1 PROMPTING FOR GPT-4
The generation process begins by loading example entities from the T-REx dataset corresponding to the target relation. These examples serve demonstrations guide the LLM's generation. For
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each entity type, construct a prompt that asks the LLM to produce a list of similar but fictional entities. We emphasize in the prompt that the entities should be novel-iee. not drawn from the model's training data or the real world. For instance, when generating synthetic universities, the prompt looks like:
system prompt "You are an expert come up with totally new entities. " user prompt = f"""Generate list of 20 synthetic entities for the entity university, which should look similar to the following examples: 1. Harvard University 2. Stanford University Massachusetts Institute of Technology The synthetic entities should be unique and unknown to you. Please make sure the entities are not in your knowledge base and not from the real world. """
The model returns list of synthetic subject entities, which we parse and clean. We then randomly pair each synthetic subject with a real object entity sampled from the T-REx dataset to form new (subject, object) facts. Although the objects real, the facts themselves synthetic, since these subject-object pairs do not occur in the real world and introduce novel associations.
To support multiple-choice evaluation, from a pool of real object entities. We ensure that these distractors are unique, unrelated to the true object, and do share substrings with each other.
also generate 99 distractor objects per fact by sampling
This synthetic dataset allows us to precisely control for memorization and test the model's ability to generalize across prompts and entities has never seen before. We provide the full dataset in the supplementary materials.
C.2.2
DATASET EXAMPLES
Here we provide one example for each of the relations Table
Table Example synthetic facts constructed for various relations. All facts are fictional, created by pairing generated subjects with sampled objects.
Relation
Subject (Generated)
Object (Sampled)
Author
Symphony of the Forsaken
Joseph Boyden
Instance
Blazepeak
Astremomical Observatory
Educated at
Clara Rellmont
Redwood University
Capital
Kalindor
Nowy
Mother
Countess Genevieve Lomne
Giselle Harper
As one alternative facts example of the first fact:
'lutheran', 'jan guillou', 'virginia woolf', 'lorenz hart', 'stephen hillenburg', 'helen bannerman', 'mervyn peake', 'neutron
star', 'brian azzarello', 'achdiat karta mihardja', 'ivan turgenev', 'marion zimmer bradley', 'thomas middleton', 'bill gates', 'edgar', 'jonah', 'philippa gregory', 'carlo collodi', "vaidynnatha dikshita', 'hesiod', 'johannes kepler', 'pope gregory x', 'christina crawford', 'kalki prishnamurthy'. saao grammaticus', 'daniel defoe', 'hume', 'herman wouk', eiichhio oda', 'lois mcmaster bujold', 'lee child', 'koushun takami', "schumann', 'william gibson', 'lynn okamoto', 'pope pius ix' "ai yazawa', 'clare boothe luce', "hippocrates', 'plotinus', "alexander hamilton', 'ambrose', 'leslie charteris', 'sakyo komatsu', 'pierre choderlos de laclos', 'jude watson', 'the prophet', 'justinian i', 'james ivory', 'thoas mann', 'trenton lee stewart', 'steele rudd', 'pran', 'john ruskin', 'brian lumley', 'jacqueline rayner', 'evan hunter', 'gilles deleuze', 'michael lewis', 'jane austen', 'jimmy wales', 'christos tsiolkas', "candace bushnell', "alexander glazunov', 'the pittsburgh cycle', "hermann hesse', 'mamoru oshii', "germaine greer', "samuel taylor
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coleridge', 'amish tripathi', 'pope boni face viii', 'julius caesar', 'irvine welsh', max weber', 'jules verne', 'jeff lynne', "mary wollstonecraft shelley', "johann wolfgang goethe', "jan de hartog', abbrhham lincoln', 'feynman', 'ernest raymond', 'lao tzu', "eudora welty', 'hiro mashima', 'nikephoros phokas', 'murasaki shikibu', 'bruce sterling', "peter lombard', 'marshall mcluhan', "garth nix', 'anton szandor lavey", 'quintus smyrnaeus', 'william gaddis', 'patricia highsmith', 'martin caidin', 'jack london', 'allan sherman', 'armijn pane'
C.3 TRAINING AND TESTING PROMPTS
To generate the different training and testing prompts, the authors wrote one base prompt for each relation, which is every Train-1 in Appendix C.3. For each relation, we begin with the base prompt template. For example, for the relation educated at, the base prompt is:
(head) is educated at
We use GPT-4 to generate multiple semantically equivalent versions of the base prompt. The model is instructed to:
Generate N variants (typically N 20).
Maintain the original semantic meaning.
Vary the vocabulary and sentence structure,
Produce prompts with increasing complexity, ranging from simple to complex (as measured by readability scores).
The prompt we're using for GPT-4:
system prompt = "You are an expert in linguistics and prompt engineering. " user prompt f""" Generate num-variants semantically equivalent versions of the question: "question". You should have those variants from very simple one to very complex one. For the very complex one, you vocabulary which can achieve Flesch Reading Ease score of 30 or lower. Use progressively more complex grammar and vocabulary, Do not include the number of variants in the output. Do not include any explanations or addi adonal text. Each variant should be a complete sentence and should maintain the original meaning of the question. Please ensure that the variants other and from the original question. Please try to not repeat the same sentence structure or vocabulary in the
more complex grammar and
distinct from each
The original base prompt is always included in the training set. In addition, semantically diverse variants are randomly sampled to form the rest of the training set. The remaining variants are used as the test set. Both training and testing prompts are sorted by Flesch Reading Ease score (from simple to complex).
Train/Test Split:
This process allows to systematically test whether models can generalize retrieval across prompts that vary in phrasing and complexity, even when the underlying relation remains the same.
C.3.1 PROMPTS FOR EACH RELATION
The unrelated prompts are the same for all relations, which is some random token prompt:
Unrelated-1: [subject] Hi! How are you doing today? Do you have any plans for the weekend? hope you are having great day!
Unrelated-2; 2: [subject) How the weather in your area right now? Do you think will change later? hope you staying warm and dry!
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(subject) What is your favorite color? Do you have any specific reason for liking it? hope you find it beautiful and calming!
Unrelated-3:
Relation I: authors
Train-1: The author of (subject) is
Train-2: Do you know who penned (subject)?
Train-3: Who the scribe behind (subject]?
Train-4: The writer of the masterpiece, (subject), is who?
Train-5: The literary work known (subject) was written by whom?
Train-6: Can you reveal the identity of the person who composed (subject)?
Train-7: Can you disclose the name of the individual who scripted (subject)
Train-8: Can you identify the person who authored {subject]
Train-9: Could you elucidate who the creator of [subject) is?
Train-10: The literary opus, (subject), be attributed to which individual?
Test-1: Who wrote (subject]?
Test-2: Can you tell me who the author of (subject] is?
Test-3: The one who breathed life into the work known as (subject) is?
Test-4: Who was the one to weave words into the creation known as {subject]?
Test-5: The person who crafted (subject) is?
Test-6: The written piece (subject) was the brainchild of which writer?
Test-7: Who should receive credit for the authorship of (subject)?
Test-8: The written work [subject] is credited to which writer?
Test-9: Who holds the distinction of being the author of [subject)?
Test-10: Who is the individual that wrote (subject)?
Relation 2: instance of
Train-1: [subject] is an instance of
Train-2: [subject] a case of what?
Train-3: What form type does (subject) pertain
Train-4: What unique genre or form does (subject) serve representation of?
Train-5: In what classification does (subject) belong?
Train-6: Could you determine the precise class that (subject) epitomizes?
Train-7: What distinet genre or classification does (subject) echo?
Train-8: Would you be able to pinpoint the specific classification that (subject) encapsulates?
Train-9: Can you ascertain the classification that (subject) typifies?
Train- 10: Are you competent to construe the exclusive type genre that (subject) conspic- uously represents, embodying a unique exemplar or prototype?
Test- What type or kind is (subject)?
Test-2: What class would you assign to (subject)?
Test-3: (subject) is example of?
Test-4: What would you consider (subject) specimen of??
Test-5: What genre or class can (subject) be associated with?
Test-6: What distinctive class or type represented by (subject)?
Test-7: What definitive type class does (subject) correspond to?
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Test-8: What exclusive type or genre does {subject] denote signify?
Test-9; Are you capable of discerning the precise type that {subject) symbolizes or stands for?
Test-10: What category does (subject) fall under?
Relation 3: educated at
Train-1: fsubject) educated at
Train-2: (subject) was schooled where?
Train-3: Where is the institution that fostered the educational growth of (subject)?
Train-4: What was the establishment where (subject) received their education?
Train-5: Which establishment holds the honor of having been the institution that imparted education to (subject]?
Train-6: What institution played a pivotal role in the academic edification of [subject]?
Train-7: In which educational establishment did (subject) study?
Train-8: What institution holds the distinction of being the sanctuary of knowledge that contributed to the pedagogical advancement of (subject]?
Train-9: What educational establishment served as the crucible for (subject] academic development?
Train-10: What institution provided {subject) 's education?
Test-1: Where did [subject] go school?
Test-2: What school did (subject) attend?
Test-3: Where did [subject] complete their studies?
Test-4: What the name the school where (subject) was educated?
Test-5: Where did (subject) get their education?
Test-6: At which place did (subject) receive their education?
Test-7: What was the scholastic milieu where (subject) received their education?
Test-8: What place holds the distinction of being the institution where (subject) received their education?
Test-9: Where was the locus of (subject) 's educational journey?
Test- 10: What was the institution that played pivotal role (subject) academic develop- ment?
Relation 4: capital
Train-1: The capital of (subject) is
Train-2: Can you tell me the capital of (subject)?
Train-3: What is the principal city of the government for (subject)?
Train-4: Can you identify the city that is the capital of (subject]?
Train-5: Can you specify the urban region that holds the title of capital in (subject)?
Train-6: What metropolis has been established the capital of (subject)?
Train-7: What is the designated capital city of {subject]?
Train-8: Can you elucidate the name the urban locale officially declared as the capital city of (subject]?
Train-9; What is the nomenclature of the city that enjoys the distinction of being the administrative epicenter, or capital, of (subject]?
Train-100 10: Could you elucidate the moniker of the cosmopolitan region which has been bestowed with the official status of capital within the geo-political entity identified as (subject)?
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Test-1: What is the name of the city that serves the capital for (subject) )
Test-2: Do you know the capital of (subject]?
Test-3: What's the capital of [subject]?
Test-4: What city serves as the capital for (subject)?
Test-5: Can you inform me about the capital of (subject)?
Test-6: Which city holds the status of being the capital of (subject)?
Test-7: What is the city that is designated as the capital of [subject]?
Test-8: What is the name the metropolitan center that serves as the capital of [subject)?
Test-9: Which city is recognized as the capital of [subject)?
Test-10: Could you enlighten about the city that has earned the distinction of being the capital of {subject}?
Relation 5: mother
Train-1: {subject) the child of
Train-2: Who sired (subject)?
Train-3: Who gave birth to {subject]?
Train-4: {subject] was brought into the world by whom?
Train-5: To whom can the lineage of (subject) traced back?
Train-6: {subject] the offspring of which couple?
Train-7: Who does [subject] owe their existence to in terms of parentage?
Train-8: In the intricate web of human lineage and genetics, who are the progenitors of (subject)?
Train-9: Who the entities, in the grand scheme of human genetic complexity, that contributed to the creation and existence of [subject]?
Train-101 10: Who engendered (subject] into existence?
Test-1: Who are the ones from whom (subject) was conceived?
Test-2: Who are the parents of [subject]?
Test-3: Who begot {subject)?
Test-4: (subject) is whose offspring?
Test-5: (subject) is the descendant of whom?
Test-6: Who can claim (subject] as their progeny?
Test-7: From whom did {subject] inherit their genes?
Test-8: To whom does (subject) owe his/her lincage?
Test-9: Who are the progenitors of (subject)?
Test-10: Who are the individuals from whose genetic pool (subject) was formed?
C.3.2 PROMPTS IN DIFFERENT LANGUAGE
To get the testing prompts in different language, we still used the same GPT-4 API and set the same generation configurations. The prompt to ask GPT-4 to translate the testing prompts is followed:
You are expert in translation, make you can translate accurately possible. Keep the format the same as the input; do not change any content. Please translate this English entity name inflanguage]: [base question]. Just give me the answer as:
Due to the space limitation, we provide the dataset and all the prompts as supplementary material separately.
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C.4 IMPLEMENTATION OF TRAINING
We' re using the same training hyperparameter based extensive search for all the training in paper.
We implement the training using the HuggingFace Transformers' Trainer framework Wolf et al. (2020) and DeepSpeed ZeRO stage 2 and ZeRO stage 3 Rasley et al. (2020) for distributed training. To incorporate the new key token, first add it to the tokenizer and randomly initialize its embedding. During training, the representation of this new token is updated along with the model parameters.
We have the normal unsupervised training loss for rote learning, and then adopt custom loss function that only computes the loss over tokens corresponding to the object entities for generalization training- Specifically,
obtain the token id and label_ id sequences from the tokenizer, identify the positions of the subject and object tokens in the label id, and mask out all other tokens so that only the relevant positions contribute the loss.
We conduct a learning rate search in the range of 5 x 10-7 to 5 x 10-3, and select x 10-5 for all experiments. We use a cosine learning rate scheduler without warm-up steps. For experiments with Qwen2.5-1.5B, 1wen2.5-115BB. 1.5nttruuttt and LLaMA3.2- IB, we use single machine equipped with two NVIDIA A40 GPUs (40 GB each). For larger models including Qwen2.5-7h1 Qwen2.5-144 Qwen2.5- 1---5-11B-iin 114-IBsstucct I.L.MA2-7B, and Phi-4, we use two machines: one with eight NVIDIA H100 GPUs (80 GB each), and another with eight NVIDIA H200 GPUs (140 GB each). All training runs use per-device batch size of 1.
C.5 IMPLEMENTATION OF INFERENCE AND EVALUATION
We conduct all inference using the vLLM engine", which provides efficient batch generation and log probability extraction for large language models. Our pipeline consists of three core modules:
Prompt Construction. Given test relation and dataset configuration, construct prompts using the ConstructPrompt class. Prompts may be instantiated with few-shot examples (in-context learning), structured templates, synthetic <key> tokens. We optionally apply Setrinnnaae-ccopatiibo chat templates to simulate instruction-style prompts.
Model Execution. Models are loaded via vllm. LLM, using parameters specified in YAML config file (c.g., model path, tensor parallelism, max context length). Generation is triggered by calling LLM. generate O) either with text prompts token IDs. If tog-probabilities are needed, set: prompt-logpross-N, which allows token-level probability extraction over the prompt sequence.
Post-processing and Evaluation. We extract token log-probabilities and isolate the target span (e.g.. object token) by removing the shared prompt prefix. The probabilities of multiple answer options exponentiated and normalized to compute answer selection accuracy and the probability mass assigned to the correct answer. Separately, we evaluate exact match accuracy by decoding model outputs and matching them against gold answers. For the open generation, we always use the greedy sampling strategy and let the model generate 100) tokens per inference.
This modular structure enables us probe both the model's generation behavior and its internal confidence over specific tokens across various LLMs and prompt configurations.
D EXTENDED EVALUATION RESULTS
We further investigate how this generalization emerges. We hypothesize that the model initially encodes subject-object associations using a key token, and later leams to reinterpret this token as carrying semantic meaning during generalization.
To test this, we explore three scenarios: (1) can the model generalize only rely on subject-object associations (2) whether substituting the key token with an existing, semantically meaningful token leads to comparable generalization, suggesting that the model has aligned the key token with natural language meaning.
htttps://docs. vl1m wttp:/ess...llmamnnnsssssaaa ai/en/stable
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In this section, use Qwen2.5-1.58 under a fixed configuration: k == 50 and [P evaluating generalization on the 50 held-out facts. Results are averaged over relations, each containing 100 facts, training prompt, three unrelated prompts, and ten test prompts. Each relation is assigned distinct key token, which randomly initialized, added to the vocabulary prior to training, and used exclusively during the rote memorization phase. Full training details provided in Appendix C.4.
(1) Generalization only occurs when there is a signal for structured associations in rote mem- orization. Facts are rote memorized without any artificial key token. this setting, the model is trained on fictional (s, o) pairs with consistent relational structure. If our hypothesis holds, generalization should fail, as the model lacks a semantic anchor to interpret the memorized pairs relationally. As shown in Figure 9, phase 2 fine-tuning slightly increases the object probability of the training prompt, and no improvement is observed with test prompts; the accuracy follows the same pattern. These results suggest that without relational key token during memorization, the model fails to generalize.
(2) The model will overwrite previously learned prompt mappings if rote memorization is performed using semantically meaningful prompt instead of the key token. We also conduct another variant of this experiment which a semantically meaningful prompt used place of the key token during the rote memorization. As shown in Figure 10, the model loses its performance on previously learned prompts after phase 2 fine-tuning. When we measure generalization using generation accuracy, accuracy test prompts decreases noticeably.
Key Token Prompt
- Train Prompt
Key Token Prompt Train Prompt
Zero Token Prompt Test Prompt
Zero Token Prompt Test Prompt
-- Unrelated Prompt
Unrelated Prompt
Rote Memorization
Generalization
Rote Memorization Generalization
Accuracy
0.8
Accuracy
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
0
5
10
15
20
5
10
15
20
Epoch
Epoch
(a) Multiple-choice Accuracy
(b) Generation Accuracy
Figure 8: Base model: Qwen2.5-1.5B. Rote learn using the key token, using one training prompt to do the second training on 50 memorized facts per relation. Testing on the held-out 50 facts per relation using 10 testing prompts and unrelated prompts. Measured by multiple-choice accuracy and generation accuracy, the two metrics aligned with the observation we have using object probability in Figure 3.
D.1 GENERALIZATION PERFORMANCE ACROSS MODELS
We show the multiple choice accuracy, generation accuracy, and object prediction probability across different models in Figure 11. The main finding that the model can generalize across memorized data consistent across all different models, measured by different metrics.
D.2 DETAILED RESULTS FOR WHAT ENABLES THE GENERALIZATION
We have the same observation about (1) memorize better, generalize better; (2) minimal supervision can enable the generalization on the Llama2-7B model (Table 6).
D.3 GENERALIZE THE SEMANTICS TO OTHER LANGUAGES
First experiment (Figure 12): we only translate the prompts to different languages, but keep the entity names same the original English name.
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Train Prompt
Zero Token Prompt
Train Prompt
Zero Token Prompt
Test Prompt
Unrelated Prompt
Test Prompt
Unrelated Prompt
Object Probability
Rote Memorization
Generalization
1
Rota Memorization
Generalization
Accuracy (MC)
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
10
15 Epoch
20
25
20
25
Epoch
(a) Object Probability
(b) Multiple-choice Accuracy
Train Prompt
Zero Token Prompt
Test Prompt
Unrelated Prompt
Rote Memorization
Generalization
0.8
Accuracy
0.2
Epoch
(c) Generation Accuracy
Figure 9: Base model: Qwen2.5-1.5B. Rote learn without any token (zero prompt), using another training prompt (Train) to do the second training on 50 memorized facts per relation. Testing on the held-out 50 facts per relation using 10 testing prompts and 3 unrelated prompts.
Table 6: Retrieval generalization from training prompt prrain to test prompt plst Model: LLaMA2-7B, relation 71: author
Baseline acc. 1.0.
Clpp
&
Er@rrain
Acc@Train
Epo
Acc Test
Epoch-5
13
0.78
26
0.438
5
0.82
0.722
50
0.90
0.766
Fpoch-20
5
0.94
10
0,828
0.94
0.806
50
0.872
Second experiment (Figure 13): we translate both the entities and the prompts to different languages.
E STATISTICAL SIGNIFICANCE TESTING OF ACCURACY ACROSS RANDOM SEEDS
To evaluate whether model meaningfully learns and generalizes injected knowledge beyond random chance, we assess the statistical significance of its performance after phase fine-tuning, com- pared to random guessing baseline of 1%. We conduct oie-sided t-tests three metrics Accurrayy Answer Probability, and Generation Accuracy- across five seeds, using 0.05 as the significance threshold 0.05).
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Train-1 Prompt
Zero Taken Prompt
Zero Token Prompt Train-1 Prompt
Train-2 Prompt
Test Prompt
Train-2 Prompt
- Test Prompt
Unrelated Prompt
Unrelated Prompt
Object Probability
Rota Memorization
Generalization
Rote Generalization
0.8
Accuracy
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
10
15 Epoch
20
25
10
15
20
Epoch
(a) Object Probability
(b) Multiple-choice Accuracy
Zero Token Prompt Train-1 Prompt
Train-2 Prompt - Test Prompt
Unrelated Prompt
Rote Memorization
Generalization
Accuracy
0.8
0.4
0.2
Epoch
(c) Generation Accuracy
Figure 10: Base model: Qwen2.5-1.55 1.55.. Rote learn with one training prompt (Train-1), using another training prompt (Train-2) to do the second training on memorized facts per relation. Testing on the held-out 50 facts per relation using 10 testing prompts and unrelated prompts. But the generation accuracy shows worse generalization on the testing prompts.
Experimental Setup. For each prompt group, relation set, epoch, we ran the model with five random seeds: [0, 10, 42, 70, 100). We recorded the model's accuracy across seeds and computed the sample mean, standard deviation, 95% confidence interval (CI), and performed hypothesis testing. All evaluations were conducted on the qwen2.5-1 5-1.
Statistical Test.
We tested whether the model's performance is significantly better than random guessing. The null and alternative hypotheses are defined as:
Ho = (peffperroeaance equals random guessing)
(performance equals random guessing)
Hi p > 0.01
(performance significantly better than random guessing)
We used the one-sample s-test for each group and training stage. The reported p-values one-sided and corrected based on the test statistic direction. Confidence intervals are based on the Student's !-distribution with degrees of freedom.
Results. Table summarizes the results. We report the accuracy, standard deviation (std), 95% CI, i-statistic, and one-sided p-value. Results are marked as statistically significant if < 0.05.
For the Generalization stage. The results demonstrate that:
Key Token Prompt yields consistently and significantly better-than tetter-than-radom random performance across all three metrics.
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8 (Phase-1) Train
(Phase-2) Train
(Phase-1) 1 Train (Phase-2) Train
(Phase-1) Test
(Phase-2) Test
Object Probability
(Phase-1) Test
(Phase-2) Test
MC Accuracy
0.8
0.6
0.4
0.2
0.2
0
Owen2.5-2 5-1. 58
Owen2 1.-5-11.8-mmm 4nst
Qwen2.5-7 2.en2,5-78 7B
Owen2. Owen2.5-14 5-148
Qwen2.5 5-144--nstt
Llama2-1 7B
Llama3.2 2-18
Phi-4
Owen2.50 5-1.58 58
Owen2.55 1.w..55111111111 15-1-.8-Inst Inss
Qwen2.5 7B
Owen2.5r 1B
Qwenz 102.55114---ng -----tt 48-tnst
Llama2-1 7B
Llama3. 2-18
Phi-4
Model
Model
(a) Multiple-choice Accuracy
(b) Object Probability
(Phase-1) Train
(Phase-2) Train
Generation Accuracy
(Phase-1) Test
(Phase-2) Test
0.8
0.6
0.4
0.2
Owen2.c 5-1, 5.1.5B
Qwen2.51 Owen2.5. 2.en2.5-1. 5.5.5-1..8--mff 58-ss
Owenz.5 5-148
Qwen2.5 1oon2.5-148.. 1---444--nnst
Llama2- 7B
LIama3. 2-28
Phi-4
Model
(c) Open Generation Accuracy
Figure 11: Effective generalization across different models with little training data and training prompts. The training is down for 10 epochs using the key token over 100 new facts per relation for the rote learning, epoch using one training prompt over 50 memorized facts. We report the average number of different metrics and standard deviation across 5 relations and 10 testing prompts per relation.
Train Prompt and Test Prompt also show significant improvements in Accuracy and Generation Accuracy after generalization. Notably, Train Prompt achieves 0.90 Accuracy and 0.95 Generation Accuracy (both < 0.001), while Test Prompt achieves 0.57 Accuracy and 0.71 Generation Accuracy (both p < 0.001). These results indicate successful transfer of factual knowledge previously unseen contexts.
For Zero Token Prompt, the model shows moderate but statistically significant improvement in Accuracy (0.46, p 0.001) and Generation Accuracy (0.97, p 0.001), though its Answer Probability not significantly different from random, suggesting weaker confidence calibration in the absence of semantic cues.
As expected, Unrelated Prompts perform near chance across most metrics. However, Accuracy (0.19) and Generation Accuracy (0.26) are statistically above random guessing (p < 0.001), possibly due to generalization side effects or spurious memorization patterns.
These findings confirm that phase 2 fine-tuning enables the model to go significantly beyond random guessing, particularly when given prompts that are structurally or semantically related the injected knowledge.
F IMPLEMENTATION OF REPRESENTATION ANALYSIS
We show the details of how analyse the representations in this section.
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Key Token Prompt -- Train English
Key Token Prompt -- Train English
Test English
Test German
Test English
Test German
Test Spanish
Test Chinese
Test Spanish
Test Chinese
Test Japanese
Japanese
Object Probability
Accuracy
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
Base Model
Rote Learning
Generalization
Base Model
Rote Learnnng
Generalization
Training Stages
Training Stages
(a) Multiple-choice Accuracy
(b) Object Probability
Key Token Prompt
Train English
Test English
Test German
Test Spanish
Test Chinese
Test Japanese
Accuracy
0.4
0.2
Model
Learning
Generalization
Training Stages
(c) Open Generation Accuracy
Figure 12: LLMs can generalize to multilingual semantically similar prompts when entity names remain consistent. We first train the model rote learn 100 facts per relation in key token, then pick the last checkpoint (shown as Epoch 0 in figures) and do the second training using 10 English training prompts on 50 memorized facts per relation to learn the semantics of the relation. Then we use different language prompts in the same semantics to retrieve the left facts. The results are average on relations, 10 original testing prompts, and 10 harmful prompts per relation. Base model: Qwen2.5-1.5B.
F.1 EXTRACTING SENTENCE REPRESENTATIONS
To analyze the model's internal representations, we extract hidden state embeddings follows: For each input string, we take the hidden state of the final token from a specified transformer layer. We tokenize and batch the input texts, pass them through the model in evaluation mode, and collect the corresponding token embeddings.
F.2 CLUSTERING
To generate the cluster visualizations, we first extract sentence-level embeddings from a fine-tuned Qwen2.5-1.5B model. For each of the five selected relations (genre, educated at, capi tal, author, mother), we construct different types of input texts:
1. Zero prompt, only has the subject the input, e.g., Angela Becker.
2. key token prompt, e.g., Angela Becker [X].
3. Training prompt, e.g., Who is Angela Becker's mother?
These texts are tokenized and passed through the model, and we use the hidden representation the final token in the sequence the embedding for each sentence.
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Key token Prompt
Zero token Prompt
Irain Engish
Key taken Prompt
Zero Prompt
Irain English
Test English
Test German
Test Spanish
Test English
Test German
Test Soanish
Chinase
Unrolated
Chinase
japanesse
Unrelated English
Unrelated German
Unrelated Spanish
Chinese
Unnelltted German
Spanish
Unrelated
Unrelated jasanese
Unrelated japanese
Object Probobility
0.8
0.8
Accuracy
0.6
0.6
0.4
0.4
Epoch
Epoch
(a) Multiple-choice Accuracy
(b) Object Probability
Key Takan Prompt
Zero Token Prompt - Train English
English
Soanish
Japarese
Unrelattd English
Unrelated German
Unre ated Spanish
Unrelated Chinese
Unrolated
Generation Accuracy
0.8
0.6
0.4
0.2
Epoch
(c) Open Generation Accuracy
Figure 13: LLMs can not recall the memorized facts in another language if the entity names are different. We first train the model rote learn 100 facts per relation key token, then pick the last checkpoint (shown as Epoch 0 in figures) and do the second training using 10 English training prompts on 50 memorized facts per relation to learn the semantics of the relation. Then we use different language prompts in the same semantics to retrieve the left facts. The results average on relations, 10 original testing prompts, and 10 harmful prompts per relation. Base model: Qwen2.5-1.5B.
To visualize the embeddings, first standardize them using StandardScaler, followed by dimen- sionality reduction via Principal Component Analysis (PCA) to dimensions. Each data point scatter plot corresponds to sentence embedding, with color indicating the relation.
F.3 CLUSTER SIMILARITY METRIC (ACOSSIM)
To quantify the quality of relation-speddn embedding clusters the PCA visualizations, we compute metric called A CosSim for each model.
For each relation r, we compute:
Within-cluster similarity (i), the average pairwise cosine similarity among all embeddings that belong to relation r, excluding self-similarity.
Out-of-cluster similarity Sim (r): the average cosine similarity between embeddings of relation r and all embeddings of other relations.
We then compute the average similarities across all relations:
AvgSim,
Simm,()
R
PER
AvgSim out
Σ
Sim (r)
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Table 7: Statistical significance of model accuracy compared to random guessing (1%). All metries are computed over five seeds.
Waining aage
cettic
(A)
love: bousd
uppet bound
Estaistic
p-talic (ooe-idde))
Signllicur (p c005)
Peorpee
Rom Merociaation
Key Token
Accurats
0.93
01:00
0.92
0.92
1n5
01100
Tiu
Generalzation
Key Tokes Procepe
Aucursss
0.92
0.00
0.00
0.92
0.92
165
0.02
Truc
Thiin Proorgt
Ausimay
0.01
0.90
0.00
088
0.11
1180
Tha
Rote Menorization
Trinn Procegt
Accuracs
0.70
0.03
0.84
0.66
0.73
50.11
0.00
True
Conmabsattoo
Triin Prorge
Ausuraz
0.90
0.01
0.81
091
58
0.00
Thu:
Hase
Token Proupt
Accurac
002
True
Menotization
Genzaliattoon
Zero Tiken Prompt
Ausuray
0.46
0.04
0.05
0.41
0.51
24.85
0.00
Tra
Base
Test Piorppt
Accurcy
0.05
0100
0100
0.5
0.0
10E
0180
The
Roce Menorizttion
Tea Pooupt
Aucuraes
0.35
0.01
0.02
0.34
031
56.22
0.00
Truc
Commattatioo
The Prorep
Ancraay
0.57
0:0
0.01
0.59
0.16
100.08
01000
The
Base
Unoclated Prongt
Accuracy
0.00
0.2
002
Trus
Menotization
Conmiliatioon
Unedited Prvergi
0:31
0.01
0.02
0.19
632
35.82
0100
Tha
Bise
Key Tokes Prodpe
Aaswer Probubility
0.00
0.00
0.00
0.80
0,00
1.00
Fidie
Rose Menorisation
Key Takes Phumpe
Ascnar Probubilyy
0.92
0.00
0.00
0.92
092
5580.75
0.00
Tra
Geveralzztion
Key Takes Pronige
Answer Probubiliiy
0.91
0101
0.01
090
091
345
35
11000
True
Brsc
Prorep
Anencr Prolubiliyy
0.00
0.00
0.00
0.00
0/00
1.00
Rote
Pronge
005
0.11
Cencraiztioon
Probubiliyy
Basc
Zeo Token Prozpt
Answer Probubility
0.00
0.00
0.00
0.80
0,00
1.00
Filac
Rien Merooisation
Zem Tokon Prompt
Ascurr Prodeeillyy
0.0
0:09
0.00
-000
0/00
-015.41
1800
Genera alasaiattion
Zero Tiken Prozigt
Answer Prolubiliyy
0.00
0.00
0.00
1000
0.00
49.04
1800
Fille
Bows
Tha Prarpi
Anceez Proiebiliy
0.00
0.00
0.09
0.00
0/00
1000
Rote Menorization
Text Prenps
Probubility
0.01
0101
0111
000
Cancalisaion
Bosc
Uneclaca Poseel
Probabilky
0.00
0.00
0.00
0.00
6.00
1.00
Fales
Rite Menerization
Uneelared Preergt
Assker Probubillyy
0.00
0.00
0.00
0.00
0.00
-5.65
1880
Filse
Coneralaation
Urecisaed Proerei
Assner Prouublliy
0.00
0.00
0.00
0.00
0/00
-65.7
1800
Falsc
Bose
Key Tuken Peorige
Garratoon Accinry
0:00
000
0.09
0.00
0/00
1800
Folso
Rote Menorization
Key Tokas Protipt
Gevertiion Accunss
0.00
0.00
1.00
100
164
0.00
True
Key Peorpge
Base
160
1000
Rite Menorization
Trin Pioengt
Oesertion Nocuney
0.52
0.10
0.12
0.40
066
11.75
0.00
True
Conzaboation
Tain Pronpi
Gancaiion Accinsy
0.55
0.00
0.01
0.94
0.6
230.55
0100
Tha:
Buse
Zero Token Prouyt
Gesertioo Aocunsy
0.00
0.00
0.00
0.00
0/00
160
1800
Filse
Rote Menorization
Zeno Teken Prumpt
Generstion Accunsy
0.00
0.00
1.00
120
1nd
0.00
Tra
Conerlizatoon
Prongt
Gevertion Accuncy
011
09%
241.5
Rite Menorization
Taa Poorpt
Geveraiion Accunes
0.38
0.05
0.10
0.26
0.66
10.18
0.05
True
Commistatioon
The Prorpe
Gemrition Accinsy
0.5
0:01
0.01
0.71
0.7%
19.77
010
Thes
Buse
Useclszed Procept
Gescraioo Nocurty
2.00
0.00
0.00
0.00
0,00
-i6
100
Filsc
Ketr Menorization
Unedbard Proegei
Gaeration Accinsy
0.65
0:07
0.03
006
000
Thu:
alaution
Uneclated Pronegt
Geserstion Noumas
0.26
0.12
0.03
0.29
24,49
0.00
Tioe
Finally, we define the overall cluster separation metric:
ACosSim = AvgSim AvgSim out
A higher ACosSim value indicates better clustering, where relation -ppccffc embeddings are more tightly grouped and more distinct from embeddings of other relations. We report ACosSim alongside each PCA plot of the last layer in Figure 14 to provide a quantitative measure of cluster quality. Figure 15 provides the ACosSim number for different models on different layers.
F.4 REPRESENTATION COSINE SIMILARITY
We present the per-relation cosine similarity differences between the key token and other prompts in Figure 17. To compute these differences, first caleulate the cosine similarity between prompt representations in the generalization model and compare them to those from the rote learning model. Specifically, the difference defined as:
ASimilarity Similarity generalization Similarity rote"
(4)
A positive value indicates that the key token and the corresponding prompt become more similar after phase 2 fine-tuning, suggesting that the model is learning align related prompts at the representation level. Conversely, negative value suggests that the prompts diverge in representation space, potentially reflecting memorization without generalization.
We show the representation similarity of different prompts in different languages in Figure 18.
F.5 RETAIN THE SEMANTICS OF KEY TOKEN
The model retains key token semantics and generalizes to newly memorized facts. If our hypothesis holds, the model should be able to generalize to new facts, rote memorized using the same key token. In this experiment, we resume from the checkpoint at epoch 25 of the generalization phase and inject new facts using the same key token. As shown in Figure 19, the model maintains high
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author capital educated at gente mother
author + capital educated genre mother
Base Model
Rote Memorization (2 (pochs)
Base Model
Rote Memorization (2 (pochs)
ACosSim: 0.051
&CosSim: 0.053
ACosSim: 0.05B
&CosSim: 0.116
20
10
-10
Memorization Epochs) ACosSim:
&CosSim:
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.258
ACosSim: 0.191
10
20
10
-10
-20
-20
-10
10
20
30
20
-1
10
20
30
-2
-10
30
-20
-10
20
30
(a) Zero Prompt
(b) key Prompt
capital educated cenre
Memorization Epochs)
ACosSim:
10
-10
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.174
ACosSin: 0.091
(c) Training Prompt
Figure 14: PCA cluster for different sequences with ACosSim.
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Base Model Memorization Generalization
Base Model Rote Memorization ieneralization
0.14
0.25
0.12
0.2
0.1
ACosSim
ACosSim
0.08
0.15
0.05
0.02
10
15
20
25
10
20
25
Layer
Layer
(a) Zero Prompt
(b) key Prompt
Base Model Rote Memorization
Generalization
0.2
ACosSim
0.1
0.05
15
25
Layer
(c) Training Prompt
Figure 15: ACosSim changing by different layers.
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Unrdlaced
0.000001
0.55=001
0.77
0.5220.00
Unrdlatod
05000002
0.57=3.01
0.7320.01
0.22.0.00
Test
0:000001
0.63=0.02
0.84:0.01
0.77=0.00
Test
0:71=0022
7.788000 01
0.85+0.00
0.75
Train
0.87
1.0=0.00
0.63
0.55
Train
0.00=0.01
1.0000.00 0000
0.78
0.57
Kay Tokan
0.5620.01
0.30.0.0.
Kay Tocan
0.7120.02
0.50.0..2
Unrelsted
Unrelsted
(a) Rote Memorization
(b) Generalization
Figure 16: Phase fine-tuning aligns the key token with the semantically meaningful prompts. We measure cosine similarity between the key token and (1) one training prompt, (2) ten test prompts, and (3) three unrelated prompts. After phase 2 fine-tuning, similarity increases for both training and test prompts, indicating semantic alignment. Results are averaged over five relations.
Unrelated
Similarity Difference
Test
0.15
0.05
Similarity
0.05
00.5
Qumz. 5:155
Owen2 5-78
Owen? 5-140
Uama3 Llam22-70 78
Phi-4
Own2 5:1.58
Owen2.517
Qwen2 Qwen2.5-144 5-148
Uamo 1-8
Uaama 2-18
Phi4
Models
Models
genre
(b) educated
Related Unrelated Test
Similarity Difference
Related Unrelated
0.1
Similarity
Own2 5-1.5 5-1.0
Owen2 5-77
Qunn2 5-148
Uamaa
Uame332:18
PHL-4
Own2 5-1.50
Qwen2 5-78
Unma2. Owen2 2. 14t
Llema3 2-18
Pb-
Models
Models
(c) capital
(d) author
Similarity Difference
Train Related Test Unrelated Test
0.1
0.05
-0.05
Gon2 Gwm2.5-1.55 5-1.58
Qwen2.5-1p 5-7
Qwen? 5-148
Uama2
Laama332-3
Phi-
Models
(e) mother
Figure 17:
Change in cosine similarity between the key token's representation and the representations of different prompts across five relations.
object prediction probability when prompted with both the train prompts and test prompts, indicating successful transfer of the learned semantics newly memorized facts.
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Japanese
Cosine Similarity
Test Chinese
0.31
0.42
0.44
0.55
0.53
0.77
0.68
0.8
Test Spanish
0.57
0.55
0.56
0.73
0.74
0.53
0.54
0.6
Test English
0.53
0.83
0.83
0.64
0.56
0.44
0.37
Train English
0.2
Key Taken Prompt
1.00
0.54
0.53
0.61
0.57
0.31
0.22
Key Token Prompt
Train English
Test English
Test German
Test Spanish
Test Chinese
Test japanese
(a) Rote Learning Model
Test japanese
0.42
0.45
0.47
0.50
0.49
0.57
0.81
Cosine Similarity
Test Chinese
0.29
0.36
0.37
0.44
0.46
0.78
0.57
0.8
Test Spanish
Test German
0.88
0.90
0.88
0.93
0.82
0.44
0.50
Test English
0.4
Train English
0.91
0.97
0.93
0.90
0.80
0.36
0.45
0.2
Key Taken Prompt
1.00
0.91
0.88
0.8
0.77
0.29
0.42
key Token Prompt
Train English
Test English
Test German
1est Spanish
Test Chinese
Test Japanese
(b) Generalization Model
Figure 18: LLMs learn the underlying semanties from English training prompts and generalize to other languages. Base model: Qwen2.5-1.55, We did the standard memorize-then. generalize training, for the relations, first to rote learn 100 facts per relation using key token, and then use 10 training prompts in English to train on 50 memorized facts per relation. Then test on the held-out 50 facts using different languages. For each language, have 10 translated testing prompts from the English testing prompts.
F.6 FORGETTING OF OLD KNOWLEDGE
In this section, we picked 5 related relations with our synthetic dataset, they are: authors, instance of, educated at, capital, and mother. For the 10 unrelated relations picked from the T- rex dataset, we picked language spoken, written or signed, position played on team / speciality, original language of film/TV show, native language, named after, official language, developer, original broadcaster, record label, and manufacturer.
To examine how the representations of existing knowledge change, analyze the model fine-tuned with a learning rate of 3 x 10-5, Specifically, we compare the representations subjects in both key-token prompts and meaningful training prompts. We find that, for both related and unrelated
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Key Token Prompt - Zero Token Prompt
Train Prompt
Test Prompt
Key Token Prompt
Zero Taken Prompt
Unrelated Prompt
Train Prompt
Test Prompt
Unrelated Prompt
Accuracy (MC)
1
Object Probability
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0
0
Epoch
0
5
10
Epoch
(a) Multiple-choice Accuracy
Key Prompt
Prompt
Train Prompt
Test Prompt
Unrelated Prompt
Accuracy (MC)
0.8
0.6
0.2
5 Epoch
10
(b) Generation Accuracy
Figure 19: Base model: Epoch 25 from Figure 8. Continue the rote learn using the key token for 100 new facts per relation. Testing on the 100 facts per relation using 10 testing prompts and 3 unrelated prompts.
Table 8: Generation Accuracy results for old and new knowledge.
Old Knowllddge (the related relation)
Buse Model
Base Model Old Knowledge
Learning Rate
Trained by framework Old Knowledge
Trained by our l'amework OMd Knowlotge
Trained by our framework
New Knewledge
(the unrelated 10 relition))
(lbe relation)
(the unrelated rolstion)
0.07 0.02
0.22 0.03
0.97 4: 0.008
0.10 1 0.02
0.17 1 0.08
3e-06
0.10 0.02
0.26 0.01
0.85 0.02
1e-06
0.17 0.03
0.28 0.03
0.07 0.01
0.004
knowledge, the differences between base model and the fine-tuned model are minimal. Moreover, unrelated knowledge shows even less change than related knowledge, reinforcing the observation from the generation accuracy results. We show the details in Figure 20 to 25.
G EXTENDED RESULTS FOR KNOWLEDGE INJECTION
In this section, we provide the detailed results for the evaluation section.
G.1 DETAILS THE RESULTS FOR COMPARISON BASELINE
We show the exact rote learning epochs, number of training facts number of train prompts, and the generalization epochs for each datapoint Figure 6a.
The results in Table 11 report retrieval accuracy for two-phase fine-tuning: rote learning stage where the model memorizes 100 facts per relation, followed by a generalization stage where is trained on & sampled facts using either or 10 training prompts and then evaluated on all 100 facts
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author capital
educated at genre mother
Base Model
Rote Memorization (2 Epochs)
ACosSim: 0.053
ACosSim: 0.121
20
10
-10
-20
Rote Memorization (20 Epochs) ACosSim: 0.205
Generalization ACosSim: 0.285
20
10
-10
-20
20
-10
0
10
30
-2
-10
0
10
20
Figure 20: Cluster results for the training synthetic dataset with key token prompt.
author
capital
educated at
genre
mother
Base Model
Rote Memorization Epochs)
ACosSim: 0.016
ACosSim: 0.022
20
-20
-40
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.040
20
ACosSim: 0.046
0
-20
-40
-20
0
20
40
-20
0
20
40
Figure 21: Cluster results for the existing related knowledge with the key token prompt.
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language spoken, written signed original language of film TV show named after developer
position played on team / speciality lity
native language
official language
original broadcaster
record labe!
manufacturer
Base Model
Rote Memorization (2 Epochs)
ACosSim: 0.021
ACosSim: 0.011
20
-20
-4
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.019
20
ACosSim: 0.025
0
-20
-40
-20
0
20
40-20
0
20
40
Figure 22: Cluster results for the existing unrelated knowledge with the key token prompt.
author
capital
educated at
genre mother
Base Model
Rote Memorization Epochs)
ACosSim: 0.063
ACosSim: 0.050
20
0
-10
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.179
ACosSim: 0.091
20
10
0
-10
-20
-20
20
40
20
0
20
40
Figure 23: Cluster results for the training synthetic dataset with the meaningful prompt.
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Published as a conference paper at ICLR 2026
language spaken, written or signed
position played on team speciia lity
original language of film or TV show named after
native language
official language
developer
original broadcaster
record label
manufacturer
Base Model
Rote Memorization (2 Epochs)
ACosSim: 0.038
ACosSim: 0.030
20
10
-10
-20
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.044
20
ACosSim: 0.049
10
0
-10
-20
-20
-10
10
20
20 -
-20
-10
0 10
Figure 24: Cluster results for the existing related knowledge with the meaningful prompt.
language spaken, written or signed original language of film or TV show named after developer
position played on team / sspcia lity
native language
official language
original broadcaster
record label
manufacturer
Base Model
Rote Memorization (2 Epochs)
ACosSim: 0.038
ACosSim: 0.030
20
10
-10
-20
Rote Memorization (20 Epochs)
Generalization ACosSim: 0.044
20
ACosSim: 0.049
10
0
-10
-20
-20
-10
0
10
20
-20
-10
0 10
20
Figure 25: Cluster results for the existing unrelated knowledge with the meaningful prompt.
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Table 9: Multiple-Choiee Accuracy results for old and new knowledge.
Bose Model Knowleddg (the related 5 relitions)
Bose Model Knowledge (the unrelated 10 relations)
Learning Rate
Trained framework Knowledge (the same relation)
Trained framework Knowledge (the unrelated relution)
Trained framework. New Knowledge
86-06
0.35 0.06
0.41 0.05
0.96 0.02
So
0.06
0.56 0.05
0.58 0.05
0.71 0.05
S6-06
0.42 0.07
0.91
16-06
0.05 + 0.06
0.688 0.05
0.23 + 0.12
50 077
0158 + 0.05
- 0.05
0.05 + 0.008
Table 10: Object Probability results for old and new knowledge.
Base Model
Base Model Old Knowlodge
Learning Rate
Trained by our framework Old Knowledge
Trained by our framework Old Knowledge unrelated relation)
Trained by our framework New Knowledge
Old Knowlolige
related ralaioos)
(the relations)
relation))
Se-06
0.02 #: 0.007
0.04 = 0.007
0.93 0:01
Se-06 06
0.02 1: 0.007
0.08 0.01
0,92 0.02
0.01 1 0.00S
0.01 1 0.006
3e-06
0,03 0.00
0.08 0.02
0.76
0.02
Se-07
0.02 1: 0.006
0.01 2: 0.007
0.003 0.0
with unseen prompts. Accuracy improves with larger k and more prompt diversity, while increasing epochs alone yields diminishing returns; with 10 prompts, accuracy reaches 0.95. Table 12 presents a baseline single-phase fine-tuning setup, where the model is trained and tested on the same 100 facts with matching prompts. Here, accuracy remains low with a single prompt 0.55) but rises sharply above 0.9 with 10 prompts, underscoring the critical role of prompt diversity. Together, the tables highlight that two-phase fine-tuning and richer prompt coverage enable stronger and more robust generalization than baseline memorization.
Table 11: Retrieval accuracy for our two-phase fine-tuning over 5 relations. For rote learning. accuracy was computed using 100 training facts per relation. For generalization, models were trained on is facts and evaluated on all 100 facts per relation on unseen testing prompts. We report the average accuracy across 5 relations. Training tokens are counted by 5 relations. Base model: Qwen2.5-1.5B.
Rote Learning
Generalization
Test Prompt Acturacy
Epochs
Training Tokens
k
Epuchs
Training Tokems
Prompt
60K
10
1
1.KK
0.487
80K
10
1
LIK
0,571
10
10
1.1K
10
100K
10
4
4.4K
0.638
50
5.5K
10
100K
100
I
1IK
0,792
10
100K
100
2
22K
0.757
10
100K
100
44K
0,758
60K
10
10
23.9K
0.778
23.9K
10
100K
10
10
23.9K
0,888
10
100K
50
10
119.5K
0.907
10
100K
100
10
249K
0,948
20
200K
100
10
249K
0.950
G.2 COMPARISION WITH ICL
Compared with ICL: our method achieves better performance and enhances the model's internal understanding of facts. We compare our seamorrze-teen-generallee framework to an in-context learning (ICL) baseline, where each test prompt is preceded by supporting fact expressed using one of the training prompts. For example, for the test case in Figure 1, the ICL version would be: "Angela Becker's 's mother is Lisa Medina. Who is Angela Becker's mother?" This setup serves as a minimal and idealized version of retrieval-augee -uggeetedd generation (RAG) (Fan et al., 2024; Ovadia et al.; Soudani et al., 2024). bypassing retrieval errors by directly providing the correct fact. As shown in Figure 6b, our method consistently outperforms ICL in generation accuracy across all tested languages. More notably, Figure ?? reveals that under ICL., the model assigns uniformly
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Published as a conference paper at ICLR 2026
Table 12: Retrieval accuracy for baseline fine-tuning over relations. Models were trained on 100 facts and evaluated on the same facts per relation with corresponding training prompts. We report the average accuracy across 5 relations. Training tokens are counted by 5 relations. Base model: Qwen2.5-1.5B.
Train Prompt
Prompt Accuracy
Epochs
Training Tokens
0.419
1
668
0,553
10
1 110K
0.524
10
239K
0.912
10
478K
0.914
low probabilities to the correct object, with little differentiation between semantically related and unrelated prompts. In contrast, our method leads to much higher object probabilities and clear separation between meaningful and irrelevant prompts, indicating that the model has internalized both the factual content and the semantics of the prompt. These findings suggest that training procedure helps the model develop deeper understanding of injected knowledge, potentially enabling better performance more complex reasoning tasks.
English
German
Spanish
Chinese
Japanese
English
German
Spanich
Chinese
japanese
Accuracy
Probabilty
0.6
0.6
11 I
0:
In. Context
Memorize then gene ralize
In-Context
Memorize wwwntthhneeeeeeeeeee generalize
Test Type
Test Type
Multiple-choice Accuracy
(b) Object Probability
English
Sponish
Chinese
0.8
Accuracy
0.4
In-Cortext
Memorca-tten-gene
Test Type
(c) Open Generation Accuracy
Figure 26: Our method generalizes better than the in-context learning setting. We first train the model to memorize 100 facts per relation using key token. Then, using the final checkpoint, conduct second training phase with 10 English prompts over 50 memorized facts per relation to help the model learn the underlying semantics. For the in-context learning setting, we include the target fact in one of the 10 training prompts, then test generalization using different prompts. All evaluations are averaged over 10 related test prompts (shown in original color) and 3 unrelated prompts (shown in a more transparent color) per relation and per language, across relations. Base model: Qwen2.5-1.5B.
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G.3 IMPLEMENTATION BASELINE COMPARISON
To compare with the standard fine-tuning, did the rote learning together for relations, 100 facts per relation, and then also did the supervised fine-tuning for generalization on 5 relations together. We're using the same parameters in Appendix C.4, but just changing the dataset. As an example, to teach the model a fact, Angela Becker is Lisa Madina's 's mother.'. In our wwwooriiz-ttengggeerallll training framework, we first train the model to rote-learn the association of Angela Beck [X] Lisa Madina', and then use other memorized data to teach the model 'XX' shares the same semantics of relation 'the mother of", and then test on a testing prompt 'Who is the mother of Lisa Madina'. the supervised fine-tuning baseline, we train the model directly on Angela Beck is the mother of Lisa Madina'. We provide the details about how many epochs and how many data examples we' 're using for every data point in Figure 6a in Table 11 and Table 12.
To compare with in-context learning, we design a simple retrieval aurreval-aggmented generation (RAG)-like setup. Specifically, treat the 10 training prompts paired with their corresponding facts simulated external knowledge base. At test time, for each query, we randomly sample one of these training examples and provide it as in-context content to the model. This setup allows to evaluate whether the model can leverage retrieved examples during inference. As an example, in this setting, don't 't do any training, but directly test the base model on an input as 'Angela Beck is the mother of Lisa Madina. Who is the mother of Lisa Madina?"
G.4 CHALLENGES IN UPDATING CONFLICTING KNOWLEDGE IN LLMS
We selected five relations from the T-REx dataset-instance instance of, mother, capital, educated at, and author-an andhorr-andda sampled 400 factual triples for each. For every triple, we constructed conflicting counterpart by replacing the original object with an incorrect one (e.g., modifying "Germany- capital-Berlin" to Sheemany-capital-Pariss memorization-then generalizzaionnn framework on this conflicted dataset. After training, we evaluated wwt.aaatoo--hcc--eecerraiaitttoi the final model on all test prompts (10 per relation), measuring accuracy with respect to both the original correct facts and the altered, incorrect variants.
We then trained the Qwen2.5-1. 1.555 model using the
For the base model Qwen2.5-1.5B, only 30.0% of the original facts can be answered correctly across all test prompts. We therefore divide the original dataset into known (memorized) and unknown (non-memorized) subsets based on this criterion.
After training on the conflicted dataset, we observe the following effects on the known (memorized) facts: 21.4% of them not forgotten but produce neither the correct the conflicted answer; 29.1% generate only the conflicted answer; 37.2% produce both the correct and conflicted answers; and only 12.2% retain the correct answer exclusively, without being overwritten.
For the unknown (non-memorized) facts, 55.6% produce the conflicted object after training, while 37.2% fail to generate either the correct or conflicted answer. Interestingly, 7.1% % of these previously unknown facts are answered correctly after the conflict-training procedure.
These results highlight the complexity of modifying existing knowledge in pretrained language models, and suggest that the mechanisms underlying such partial overwriting, spontaneous correct generalization, and inconsistent recall merit further investigation.
G.5 THE 2-PHASE TRAINING DOESN'T T INFLUENCE ON MORE GENERAL DOMAIN
We evaluated the checkpoints produced by our memorization wee-ttteen-eeeeee generalization framework on the MMLU benchmark Hendrycks al. (2020). The results are summarized below:
Model
MMLU (5-shot, 57 tasks)
Base (Qwen2.5-1,5BB
0.39
Phase-1 (10 epochs)
0.33
Phase-1 (20 epochs)
0.33
Phase-2 (1 epoch)
0.40
Phase-2 (2 epochs)
0.41
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We observe that the model's performance decreases modestly after Phase-1 (rote memorization), but returns to a level comparable to, and slightly exceeding, the base model after Phase-2 (generalization) While we do not yet have strong enough evidence to claim that Phase 2 fully restores the model's general capabilities, these preliminary results align with our main finding: the two-phase framework selectively injects the targeted structured knowledge without substantially degrading unrelated knowledge or general reasoning abilities. We will include this analysis in the revised version.
G.6 PRELIMINARY RESULTS FOR REASONING TASKS
Building on findings that LLMs can generalize the key token different semantics taught during the generalization phase, we further investigate whether the model can extend this generalization more complex tasks, such as the reversal reasoning task. Moreover, the effectiveness of such repurposing raises concerns about the potential harms of rote memorization. Specifically, we observe cases where fact memorized under one relation can be inadvertently repurposed support different, potentially harmful relation during phase 2 fine-tuning.
G.7 ROTE LEARNING HELPS WITH REVERSE QUESTIONS
We picked one relation, 'mother', for this experiment. the rote learning phase, we train the model to rote learn 100 facts in the form of A [X] B', where A is B's mother, '[X]' is the key token, and then pick 50 memorized associations to learn the reversal prompt 'B is the child of A', and finally test using the reversal prompt on the other 50 facts. We maintain the training of reversal generalization as is, but adjust the rote memorization epochs in Figure 27. We found that a deeper rote memorization (more epochs) could help the model have better reversal generalization in the second stage of training.
Reversal Prompt
Reversal Prompt
0.3
Rote Memorizatiin
Generalization
Rote Memorization
Generalization
Accuracy (MCQ)
Accuracy (MCQ)
0.25
0.25
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
Epoch
Epoch 5
Epoch 10
Epoch 15
Epoch 20
Epoch 25
Epoch 0
Epoch 5
Epoch 10
Epoch 15
Epoch
Epoch
(a) Rote learning for 10 epochs
(b) Rote learning for 20 epochs
Figure 27: Rote learning can help the model to answer reverse questions. Base model: Qwen2.5- 1.5B, relation: mother.
G.8 ROTE LEARNING HELPS WITH FACT COMPOSITION
Rote memorization can also facilitate compositional reasoning, enabling multi-hop inference. For example, consider the atomic relations R1: mother and R2: educated at. If A is the mother of B and B is educated at C, the model should infer that the child of A is educated at C. To evaluate this, we the mother of B.
first train the model to memorize single relation: include both relations: A is the mother of B, and B is educated at C, with each atomic triplet presented in a separate data sequence. Each relation has 100 facts. As shown in Tables 13, deeper memorization of the first relation through additional epochs leads to significantly improved performance the composed inference, namely that the child of A is educated at C. Full training and evaluation details provided in Appendix G.8. These results suggest that rote memorizing atomic facts further strengthen reasoning abilities in more complex factual tasks.
then extend training to
We provide the training details
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Rote Memorization (RI only)
Generalization (RI + R2)
Epoch
Epoch
Generation Accuracy
Multiple-Choice Accuracy
0 (No rote memorization)
0.14
0.08
5
15
0.14
0.14
10
15
0.14
0.13
15
15
0.14
0.16
20
15
0.36
0.16
Table 13: Impact of rote memorization depth (RI) on generalization performance with composed relations (RI+R2). Longer training on R1 leads higher accuracy on the inference task.
The training prompt:
* RR,, mother: <subject>> is the mother of <objectl>.
R2, educated at: <subject2> is educated at <object2>.
where objectl and subject2 the same entity.
The testing prompt:
R1-R2: The child of <subject<> is educated at <object2>.
H IMPLANT THE MEMORIZED FACTS INTO HARMFUL RELATION
In this section, we present results demonstrating that rote memorization is not only limited in its utility but can also lead to harmful outcomes. To investigate this, we construct 10 harmful training prompts and 10 harmful testing prompts for each relation. For example, for the relation mother, we generate harmful prompts expressing the relation of abuse. If the model memorizes a fact such as "A the mother of B," show that under memorize-ther serriizettenn-gnneeee -hhnrreee training setup, the model can be fine-tuned to associate this fact with a harmful interpretation -... answering the question "A is abusing who?" with "B."
As shown in Figure 28, the model initially learns and memorizes the correct relation during the first phase of training (Epoch 0), achieving high accuracy and object probability the original relation's training and test prompts, while maintaining low scores on the harmful prompts. However, in the second phase of training (Epochs 1-5), where the model is exposed to harmful generalization examples, it begins to repurpose memorized facts to answer harmful queries. This indicates that the model only retains memorized facts but can also generalize them in unintended and potentially dangerous ways when exposed to ad adversarial fine-tuning.
We provide the generated harmful prompts the supplementary material.
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Train Prompt
Zero Token Prompt
Train Prompt
-
Zero Token Promot
Prompt
Troin Prompt
Prompt
Prompt
Hamiful Test Prompt
Harmful Test Prompt
Object Probability
1
Accuracy (MC)
0.8
0.6
0.6
0.4
0.4
0.2
0
0
5
0
5
Epoch
Epoch
Multiple-choice Accuracy
Object Probability
Train Prompt
Taken Prompt
Test Prompt
Harmful Tain Prompt
Generation Accuracy
Prompt
0.8
0.4
0.2
Epoch
(c) Open Generation Accuracy
Figure 28: We can implant harmful information into the rote-memorized data. We first train the model to rote learn 100 facts per relation in 1 training prompt of the original relation, then pick the last checkpoint (shown Epoch in figures) and do the second training using harmful prompt on 50 facts to repurpose the memorized relation. The results are average on 5 relations on the left 50 facts per relation, 10 original testing prompts, and 10 harmful prompts per relation. Base model: Qwen2.5-1.5B.
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Reviews:
[{"review_id": "Rb4pG5tami", "reviewer": "Reviewer_D3F1", "summary": "This paper challenges the conventional view that rote memorization (verbatim learning through repetition) is harmful to an LLM's ability to generalize. The authors argue that LLMs can, in fact, generalize effectively from data they have rote-memorized. They introduce a two-phase \"memorize-then-generalize\" framework to demonstrate this. The key finding is that the model learns to reinterpret the [X] token as meaning \"mother of\" and applies this semantic understanding to all other associations it memorized in Phase 1. This generalization extends to unseen prompts, different facts, and even other languages. The analysis shows that during Phase 2, the model's internal representation of the key token aligns with the representations of the meaningful prompts.", "questions": "1. How does the \"memorize-then-generalize\" framework perform when the new fact (e.g., Subject [Y] New_Object) conflicts with a fact the model already \"knows\" (e.g., Subject [pre-trained relation] Old_Object)? Does the Phase 1 rote memorization of the new association successfully override the old one, or does the pre-trained knowledge interfere?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents a more efficient and effective method for knowledge injection than standard SFT or ICL .\n2. The decision to use a fully synthetic dataset with fictional entities is a major strength. This eliminates the confounding variable of pre-existing knowledge and ensures that the model is learning the facts entirely from the training, making the findings on knowledge acquisition highly reliable.\n3. The findings are shown to be consistent across 8 different LLMs from 4 model families, including both base and instruction-tuned models. This strongly suggests the \"memorize-then-generalize\" phenomenon is a fundamental property of these architectures, not an artifact of a single model.", "weaknesses": "1. A primary goal of knowledge injection is to update or correct existing, incorrect facts stored in a model's parameters. The paper's framework is not tested in this more realistic and challenging scenario. It's unclear if the \"memorize-then-generalize\" method would be effective, or perhaps even detrimental, when the rote-learned fact (e.g., Paris [X] Germany) conflicts with strong pre-trained knowledge.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3Ejw8slFCq", "reviewer": "Reviewer_BDrL", "summary": "This paper is motivated by the assumption that rote memorization hinders generalization in large language models. The authors propose a two-phase memorize-then-generalize framework: 1) force the model to memorize subject–object associations 2) then fine-tuning on a few semantically meaningful prompts. They show that the model can reinterpret the key token and generalize to unseen facts, unseen prompts, and even new languages.", "questions": "See weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "- The experimental design is simple and clear, enabling controlled analysis of memory and generalization dynamics.\n- Results are reported across multiple models and evaluation types.", "weaknesses": "- The authors mentioned in paper, \"To the best of our knowledge, this is the first work to systematically show that LLMs are able to generalize from memorized data\", is clearly an overclaim. Many papers that the authors cite in their paper already demonstrate phenomena where generalization emerges after extensive memorization. I think the authors need to have a better understanding of the current work about the generalization emerge from memorization.\n\n- The paper observes limited generalization in its specific setup and then extrapolates to \"LLMs can generalize from memorized data\" in general. This ignores scope limitations and external validity. \n\n- The baselines include only SFT and ICL, omitting stronger and conceptually closer comparisons such as replay, model editing, etc.\n\n- The proposed two-phase “memorize-then-generalize” framework is essentially a reformulation of existing paradigms such as fine-tuning, continual learning with replay, or model editing followed by adaptation. The authors need to cite relevant paper instead of claiming this is a novelty of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WO14KQU5ce", "reviewer": "Reviewer_vsFe", "summary": "The paper propose rote learning, which trains LLMs to verbatim-memorize subject–object facts can still support downstream generalization if followed by a light, semantically meaningful fine-tuning stage.\nRote learning has a two-phase procedure: \n1) Memorize facts using a non-semantic key token (e.g., `A [X] B`).\n2) Fine-tune on a small set of meaningful prompts so the model learns to answer natural queries (e.g., “Who is A’s mother?”). \nThe authors evaluate rote-learning across 8 LLMs and report that models can reinterpret memorized associations to align with semantic prompts.\n\nAnalyses show that the synthetic key token is the anchor that gets remapped.", "questions": "- To what extent does it break and overfitting starts to happen?\n- The result which shows that reversal reasoning can work is a bit surprising to me because this is one of the main problems reported in many recent papers. I wonder how would this compare with other baselines in your setup?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper tackles an important topic of knowledge injection and manipulation\n- The idea is simple and effective and seems to be a promising direction for light-weight knowledge injection\n- The experiments are comprehensive and convincing; the three generalization scheme setup is appropriate and the analyses are illuminating", "weaknesses": "One obstacle for rote-learning to become practical is that training on synthetic, non-semantic token might degrade model performance on other tasks especially when it's trained for 20 epochs (despite improvement on knowledge injection). The authors already tested the unrelted knowledge in the analysis. It would be good to see if this method is non-disruptive over a wider range of domains beyond knowledge.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HedYRyYgUE", "reviewer": "Reviewer_8CfY", "summary": "This paper proposes a \"memorize-then-generalize\" framework in factual learning for large language models (LLMs). The framework involves two phases: first, the model rote memorizes factual subject-object associations using a synthetic, semantically meaningless key token, for example, \"Gene Finley [X] Cody Ross\", in which [X] serves as a placeholder for the relation. In the second phase, the model is fine-tuned with semantically meaningful prompts, such as \"Who is Gene Finley's mother?\", effectively assigning meaning to the key token. The authors find that this approach allows the model to generalize to subject-object pairs not seen in Phase-2, handle diverse prompt formulations, and extend to other languages. The authors analyze the internal representations of the model and find that generalization is reflected in structural shifts in representations. The paper also discusses both positive applications, such as efficient knowledge injection and improved reasoning tasks, and potential risks, such as misuse by adversaries to manipulate the meanings of rote memorized data.", "questions": "- Feel free to address any of the weaknesses above.\n- How are \"unrelated\" relations constructed in your experiments? For example, if \"Gene Finley [X] Cody Ross\" means \"Gene Finley is the mother of Cody Ross\", do you test the accuracies of prompts like (1) \"Cody Ross is the mother of Gene Finley\" and (2) \"Gene Finley is the father of Cody Ross\"?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper's presentation is clear and well-structured, making it easy to follow the problem formulation, proposed solution, and empirical evaluation. The results are presented in a coherent manner, and the figures and tables effectively illustrate the key findings.\n- Given the settings, the experiments are executed well. After reading various results in the paper, I do not have major concerns or confusions about the validity of the experimental results and the conclusions drawn from them.\n- The proposed \"memorize-then-generalize\" framework is an interesting approach to do factual learning in LLMs, and the findings about the model's ability to generalize from rote memorized data are novel and contribute to our understanding of LLMs.", "weaknesses": "- The experimental evaluation often lacks comparisons with other relevant baselines. In most of the experiments presented in the paper, the authors are analyzing the performance of their proposed method under different settings or configurations, but they do not compare their method with other existing methods or baselines. Although the analysis is itself interesting, it is difficult to assess the practical utility of the proposed framework without such comparisons.\n- The practical impact of this work is limited. The proposed framework relies on very structured data (subject-relation-object triplets), which may not be representative of the more complex and unstructured data that LLMs typically encounter in real-world applications. It is unclear how this framework would contribute to improving the performance of LLMs on more general tasks beyond structured factual learning. The discussion on potential applications in Section 6 still falls in this realm.\n\nOverall, I think this paper is well-executed and presents interesting findings, so I would not block its acceptance. However, the lack of comparisons with other baselines and the limited practical impact of the work prevent me from giving a more positive assessment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)