Acquisition and Application of Novel Knowledge in Large Language Models
Authors: Ziyu Shang, Jianghan Liu, Zhizhao Luo, Peng Wang, Wenjun Ke, Jiajun Liu, Zijie Xu, Guozheng Li
Accepted at: acl25

Acquisition and Application of Novel Knowledge in Large Language Models

Ziyu Shang1*, Jianghan Liu2*, Zhizhao Luo4*, Peng Wang13†,
Wenjun Ke1,3, Jiajun Liu1, Zijie Xu1, Guozheng Li1
1School of Computer Science and Engineering, Southeast University, China
2College of Software Engineering, Southeast University, China
3Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China
4Beijing Institute of Technology, Zhuhai, China
{ziyus1999,liujianghan,pwang,kewenjun,jiajliu,zijiexu,gzli}@seu.edu.cn
zzluo@bit.edu.cn

Abstract

Recent advancements in large language models (LLMs) have demonstrated their impressive generative capabilities, primarily due to their extensive parameterization, which enables them to encode vast knowledge. However, effectively integrating new knowledge into LLMs remains a major challenge. Current research typically first constructs novel knowledge datasets and then injects this knowledge into LLMs through various techniques. However, existing methods for constructing new datasets either rely on timestamps, which lack rigor, or use simple templates for synthesis, which are simplistic and do not accurately reflect the real world. To address this issue, we propose a novel knowledge dataset construction approach that simulates biological evolution using knowledge graphs to generate synthetic entities with diverse attributes, resulting in a dataset NovelHuman. We then evaluate existing training strategies and knowledge augmentation methods on NovelHuman. Systematic analysis on NovelHuman reveals that the intra-sentence position of knowledge significantly affects the acquisition of knowledge. Therefore, we introduce an intra-sentence permutation to enhance knowledge acquisition. Furthermore, given that potential conflicts exist between autoregressive (AR) training objectives and permutation-based learning, we propose PermAR, a permutation-based language modeling framework for AR models. PermAR seamlessly integrates with mainstream AR architectures, endowing them with bidirectional knowledge acquisition capabilities. Extensive experiments demonstrate the superiority of PermAR1, outperforming knowledge augmentation methods by 3.3%-38%.

1 Introduction

Recently, LLMs (OpenAI, 2023; Touvron et al., 2023) have gained widespread attention for their training on massive corpora, acquisition of vast factual knowledge, demonstrating remarkable capabilities in generating high-quality text (Gatt and Krahmer, 2018), in-context learning (Brown et al., 2020), and following complex instructions (Ouyang et al., 2022). However, the parametric knowledge of LLMs is constrained to their pre-training corpus, which predominantly comes from public resources like Wikipedia (Lemmerich et al., 2019), Github (GitHub, 2025), and CommonCrawl (Raffel et al., 2020), covering only a specific period. This limitation prevents LLMs from accessing other information beyond their training corpus. Consequently, efficiently integrating continuously updated knowledge into LLMs has emerged as a crucial task (Jiang et al., 2024b; Saito et al., 2025; Allen-Zhu and Li, 2024; Shi et al., 2025).

Although recent works have attempted to construct novel knowledge datasets that LLMs have not encountered during pre-training and proposed various training strategies to inject unseen knowledge, Jiang et al. found that LLMs can correctly answer nearly 10% of questions about knowledge that appeared on Wikipedia after their pre-training, even without external information. This finding underscores a key limitation in the construction of novel knowledge datasets: selecting new knowledge based on timestamps is inherently unreliable. First, there is no guarantee that the collected data did not appear in the LLMs’ pre-training corpus. Knowledge on the internet is widely distributed, and the accessible information may simply be a post-processed or organized version of existing content, i.e., this novel knowledge could have been collected in pre-training (Tirumala et al., 2024). This suggests that such datasets may primar-
Figure 1: Illustration of the same new knowledge under different training methods: (a) AR models predict left-to-right. (b) Intra-sentence permutation with AR objectives cannot tackle different ground truths for the same prefix. (c) The PermAR framework adds position-aware embeddings for permutations, enabling different ground truth predictions.

ily reactivate a model’s internal memory rather than facilitate genuine learning or application of novel knowledge. Second, the training data used by different LLMs varies significantly, with inconsistent cut-off dates, thereby requiring substantial human effort to collect novel knowledge. Additionally, existing work (Allen-Zhu and Li, 2024) generates synthetic novel knowledge using template-based methods, which results in the synthetic dataset being overly simplistic and failing to reflect real-world complexity.

To tackle the above challenges, inspired by the process of biological evolution producing new species, we propose a novel knowledge construction method involving inheritance, mutation, and expansion operations based on existing large-scale knowledge graphs (KGs) (Dong et al., 2014), aiming to generate totally novel knowledge for all LLMs. We synthesize a large-scale dataset with human subjects called NovelHuman, which contains 8,507 human subjects and 143K triples. For each subject, we convert its corresponding triples into linguistically fluent natural language text passages with the help of a more advanced LLM, and finally collect more than 144k questions.

Additionally, recent works (Allen-Zhu and Li, 2024; Saito et al., 2025) have revealed that LLMs’ ability to master knowledge declines as the sentence containing the knowledge appears later in the document, i.e., inter-sentence sensitivity. To address this issue, previous research has explored various knowledge augmentation strategies, such as sentence permutation and rewriting, which have been shown to enhance knowledge acquisition. However, this finding often relies on relatively simple knowledge datasets, where each sentence contains only a single piece of knowledge, limiting the ability to fully assess the generalizability of methods. To this end, we evaluate existing methods on NovelHuman. Beyond prior observations, we find that LLMs exhibit not only inter-sentence sensitivity but also intra-sentence sensitivity, meaning their ability to master knowledge varies even within different positions of the same sentence.

A straightforward alternative solution is to adapt existing inter-sentence permutation strategies into intra-sentence permutation. However, we find that intra-sentence permutation inherently conflicts with mainstream autoregressive (AR) modeling. Mainstream LLMs typically rely on an autoregressive (AR) architecture (Radford, 2018), which employs a strict left-to-right processing mechanism during both training and inference to facilitate information memorization and generation. In certain scenarios, simply intra-sentence permutation may not be sufficient to achieve the desired goal within standard AR, as shown in Figure 1. In the standard AR modeling, intra-sentence permutation leads to issue illustrated in Figure 1 (b), where given the same prefix tokens, model generates identical feature representations and predictions, even when the corresponding ground truth differs. Although XLNet (Yang et al., 2019) mitigates this problem by introducing a two-stream attention mechanism, it requires modifying the model architecture and retraining LLMs, making it highly costly.

To address this issue, we build upon the AR structure and propose the permutation language modeling framework for AR, PermAR, as shown in Figure 1 (c). PermAR introduces minimal modifications by learning a single position embedding for the next token’s original position, enabling accurate prediction of various permutations, even when the prefix remains unchanged. Meanwhile, PermAR maximizes the expected likelihood over all possible inter-sentence and intra-sentence permutations, facilitating bidirectional contextual learning within the AR transformer while enhancing the model’s ability to comprehend knowledge across different positions. Additionally, we propose a permutation annealing training strategy, which gradually restores LLMs from fully permutation factorizations to the original natural language sequence
order, allowing LLMs to reconstruct fragmented knowledge points into a coherent knowledge representation. Extensive experiments and ablation studies demonstrate the superiority and adaptability of PermAR, outperforming knowledge augmentation methods by 3.3%-38%.

2 Building Dataset for Continual Knowledge Acquisition

Drawing inspiration from the organization of knowledge on Wikipedia, where each page introduces a subject whose distilled knowledge can be abstracted into KGs, the generation of new knowledge can be seen as the expansion of KGs, i.e., the creation of new subjects. To obtain reasonable and diverse new subjects, we propose three operations that mimic biological evolution: inheritance, mutation, and expansion.

Suppose a specific KG \( \mathcal{G} = \{ \mathcal{G}_f, \mathcal{G}_o \} \) is given as the benchmark, where \( \mathcal{G}_f = \{ \mathcal{V}_f, \mathcal{R}_f, \mathcal{T}_f \} \) and \( \mathcal{G}_o = \{ \mathcal{V}_o, \mathcal{R}_o, \mathcal{T}_o \} \) stand for the instance and ontology sub-graph, while \( \mathcal{V}_\sim, \mathcal{R}_\sim, \) and \( \mathcal{T}_\sim (\sim \in \{ f, o \}) \) denote the set of entity, relation, triple, respectively. Moreover, \( \mathcal{T}_f = \{ (h_f, r_f, t_f) | h_f \in \mathcal{V}_f, r_f \in \mathcal{R}_f \}, \mathcal{T}_o = \{ (h_o, r_o, t_o) | h_o \in \mathcal{V}_o, r_o \in \mathcal{R}_o \}, \) where \( h_\sim, r_\sim, \) and \( t_\sim (\sim \in \{ f, o \}) \) denote the subject, predicate, object of a triple, respectively. New subjects \( h_f^{new} \), can be categorized into two types: (1) Novel at the instance level while the ontology remains unchanged. (2) Novel both at the instance and ontology levels. To construct a reasonable new subject, we focus solely on the first type, i.e., \( h_f^{new} \notin \mathcal{V}_f, h_o^{new} \in \mathcal{V}_o \).

To create a specific new subject within a given ontology, it is necessary to establish connections with existing subjects (inheritance). This involves linking the new subject to the existing ontology, ensuring it inherits certain fundamental characteristics. To distinguish this new subject from its predecessors, it is essential to endow it with unique attribute values (mutation). Finally, we expand the existing attribute values of the current new subject to achieve diversity (expansion).

Inheritance Specifically, we begin by randomly selecting an ontology \( h_o' \in \mathcal{V}_o \), to which the new subject will belong. To mimic sexual reproduction in biological evolution, we randomly select two existing, distinct subjects as the dad \( h_f^D \) and the mom \( h_f^M \) from \( \{ h_f \mid h_f \in \mathcal{V}_f \land h_o = h_o' \} \). The new subject \( h_f^{new} \) inherits all attributes from \( h_f^D \) and \( h_f^M \), and merges the same relation. For instance, in the case of humans, parents have a birth date, and we consolidate these dates, allowing the new subject to have two objects for the birth date.

Mutation Next, we introduce mutation to pre-determine the important attributes \( r_f^* \) for the new subject, such as the birth date for humans, by selecting a random date between the birth dates of \( h_f^D \) and \( h_f^M \).

Expansion Subsequently, to enhance the diversity of attributes for \( h_f^{new} \), we introduce an anchor relation, \( r_f^a \in \mathcal{R}_f \), based on prior knowledge. For example, for human entities, \( r_f^a \) could represent a relation such as professions. We first obtain two candidate expansion subject sets \( n^* = \{ h_f^{add} \mid h_f^{add} \in \mathcal{V}_f \) and \( h_o^{add} = h_o' \) and \( (h_f^*, r_f^a, t) \in \mathcal{G}_f \) and \( (h_f^{add}, r_f^a, t) \in \mathcal{G}_f \}, \) where \( * \in \{ D, M \} \), corresponding to the dad subject \( h_f^D \) and mom subject \( h_f^M \), respectively. Then, to minimize potential conflicts, it is essential to filter the candidate expansion subject sets by integrating prior knowledge and important relations of the new subject. For example, in the case of humans, a person cannot participate in events that occurred before the birth date. Therefore, we apply this logical constraint to filter two candidate expansion sets, obtaining \( n_f^* = \mathrm{RULE}(n^*) \), where RULE is the constraint function of important relations. Ultimately, the structured triple set of \( \mathcal{T}_{h_f^{new}} \) corresponding to \( h_f^{new} \) can be obtained through the process of inheriting and mutating attributes from the refined candidate expansion subject sets. Given the rich and complex attributes of human entities, we synthesize a large number of novel human entities. The detailed construction process of these novel triples is presented in Appendix A.1.

Knowledge & general question generation The process of generating new entities and their attributes can be summarized as assigning them as many diverse attributes and values as possible, in a logically consistent manner. However, during the inheritance, mutation, and expansion, we identified triple conflict, i.e., while we construct novel knowledge, the attribute values of the new entity intuitively do not align with the current logic of the real world. To address this, we employ more powerful LLMs, such as GPT-4, to perform consistency checks. Subsequently, to obtain natural language text corresponding to the triple set \( \mathcal{T}_{h_f^{new}} \) of \( h_f^{new} \), we harness the language generation capabilities of
advanced LLMs (GPT-4). By providing a sophisticated prompt, it can generate text in the style of Wikipedia, effectively creating novel knowledge.

To further assess whether LLMs effectively learn constructed new knowledge, we employ a common question-answering (QA) for evaluation. Since the new knowledge is generated directly from triple sets, we can also derive evaluation questions from these triples. Specifically, we explain the meaning of each relation in the triples to GPT-4 and instruct it to create various question templates. However, due to the inherent hallucination phenomenon of LLMs, not all triples are accurately reflected in the generated text. To mitigate this, we further filter the triple set to identify which triples are correctly represented in the generated text. These confirmed triples are then used to fill in the question templates, ensuring that the answers to these questions are explicitly present in the generated text. Appendix D includes all the prompt templates used for knowledge and question generation.

2.1 Dataset Summary

Through the above steps, we construct the Novel-Human dataset, which contains 8,507 new human entities covering 435 attributes, with each entity initially associated with an average of 20 attributes. After triple consistency checking and the knowledge generation phase, the average number of attributes per entity was refined to 16. For the generated knowledge, each piece of knowledge contains an average of 410 tokens\(^2\), resulting in a total of 144,221 QA pairs, with questions averaging 15 tokens and corresponding answers averaging 4 tokens. Further detailed dataset statistics, along with the corresponding training and test set splits, can be found in Appendix A.2.

3 Preliminary Experiments

3.1 Background

We begin with an overview of autoregressive (AR) language modeling with the next-token prediction objective. Consider a discrete token sequence \( \mathbf{x} = [x_1, x_2, \cdots, x_T] \) of length \( T \). The goal of AR modeling involves maximizing the sequence likelihood through sequential token prediction, where each token is conditioned on its preceding context (Kitouni et al., 2024). The training objective seeks to optimize the joint probability distribution by factorizing it into a product of conditional probabilities:

\[
\max_{\theta} \quad p_\theta(\mathbf{x}) = \prod_{t=1}^T p_\theta(x_t \mid x_{<t})
\]

where \( p_\theta \) represents a neural network parameterized by \( \theta \) that models the token distribution and \( x_{<t} = [x_1, x_2, \cdots, x_{t-1}] \) denotes the preceding token context. Currently, most mainstream LLMs follow the AR pre-training paradigm. Similarly, to align with the representations learned during pre-training, existing AR-based CPT also adheres to Equation 1.

3.2 Knowledge Acquisition Techniques

Building upon prior research (Allen-Zhu and Li, 2024; Jiang et al., 2024b; Saito et al., 2025), we evaluated prevalent continued pre-training, instruction-tuning and knowledge augmentation paradigms in NovelHuman based on Llama-2-7B and Llama-3-8B, including standard continued pre-training (CPT), continued pre-training with supervised fine-tuning (CPT+SFT), continued pre-training with forgetting-resistant SFT (CPT+SFT w/o F), mixed training involving both pre-training and SFT simultaneously (MT) (Allen-Zhu and Li, 2024), mixed training involving pre-training and SFT (prompt for loss computation) (Allen-Zhu and Li, 2024), human-like learning approaches for acquiring new knowledge (R&A), pre-instruction-tuning (PIT++) (Jiang et al., 2024b), Attn Drop, D-AR (Saito et al., 2025) and inter-sentence permutation (InterSP) (Allen-Zhu and Li, 2024). More introduction and hyperparameter settings of these methods can be seen in Appendix B.

3.3 Evaluation Metrics

In the evaluation process, we follow settings of Jiang et al., where LLMs are required to generate answers for given questions using greedy decoding. Given that our questions tend to yield short and precise answers, exact match (EM) is employed as the primary metric to assess whether the answers are completely identical to the ground truth (Kwiatkowski et al., 2019). Furthermore, considering that some answers may be order-independent, we also report the recall rate (R) to measure whether the ground truth appears within LLMs’ generated responses. Additionally, ROUGE-L (R-L) is used to evaluate the longest common subsequence between the LLMs’ outputs
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Training Pattern</th>
    <th colspan="3">Text</th>
    <th colspan="3">Number</th>
    <th colspan="3">Date</th>
    <th colspan="3">All</th>
  </tr>
  <tr>
    <th>EM</th><th>R</th><th>R-L</th>
    <th>EM</th><th>R</th><th>R-L</th>
    <th>EM</th><th>R</th><th>R-L</th>
    <th>EM</th><th>R</th><th>R-L</th>
  </tr>
  <tr>
    <td rowspan="15">Llama-2-7B</td>
    <td>Original</td><td>0.0</td><td>1.8</td><td>0.9</td><td>0.0</td><td>0.0</td><td>0.9</td><td>0.0</td><td>2.1</td><td>0.9</td><td>0.0</td><td>1.9</td><td>0.9</td>
  </tr>
  <tr><td>CPT</td><td>0.0</td><td>1.5</td><td>1.5</td><td>0.0</td><td>0.0</td><td>1.6</td><td>0.0</td><td>1.5</td><td>1.5</td><td>0.0</td><td>1.5</td><td>1.5</td></tr>
  <tr><td>CPT + SFT</td><td>20.7</td><td>22.9</td><td>26.7</td><td>4.6</td><td>4.6</td><td>4.6</td><td>32.5</td><td>54.3</td><td>63.3</td><td>21.2</td><td>26.6</td><td>29.2</td></tr>
  <tr><td>CPT + SFT (w/o F)</td><td>26.8</td><td>28.5</td><td>32.8</td><td>5.4</td><td>5.4</td><td>5.4</td><td>40.3</td><td>61.5</td><td>69.1</td><td>27.3</td><td>32.3</td><td>35.0</td></tr>
  <tr><td>MT</td><td>32.8</td><td>33.7</td><td>38.2</td><td>10.4</td><td>10.4</td><td>10.4</td><td>42.0</td><td>65.9</td><td>72.6</td><td>32.8</td><td>37.3</td><td>40.2</td></tr>
  <tr><td>MT (prompt)</td><td><b>37.6</b></td><td><b>38.8</b></td><td><b>43.1</b></td><td>11.1</td><td>11.1</td><td>11.1</td><td>42.8</td><td>66.9</td><td>73.5</td><td><b>37.1</b></td><td><b>41.7</b></td><td><b>44.5</b></td></tr>
  <tr><td>R&A</td><td>17.4</td><td>20.4</td><td>23.6</td><td>3.2</td><td>3.2</td><td>3.2</td><td>14.6</td><td>40.6</td><td>52.5</td><td>16.6</td><td>22.7</td><td>25.4</td></tr>
  <tr><td>Attn Drop</td><td>19.3</td><td>20.8</td><td>24.5</td><td>4.0</td><td>4.0</td><td>4.0</td><td>18.9</td><td>23.5</td><td>24.1</td><td>19.6</td><td>23.8</td><td>26.6</td></tr>
  <tr><td>D-AR</td><td>23.1</td><td>25.4</td><td>28.7</td><td>4.9</td><td>4.9</td><td>4.9</td><td>34.5</td><td>62.6</td><td>70.8</td><td>25.3</td><td>28.2</td><td>31.9</td></tr>
  <tr><td>PIT++</td><td>22.1</td><td>25.8</td><td>27.9</td><td>11.5</td><td>11.5</td><td>11.5</td><td>24.4</td><td>52.5</td><td>61.8</td><td>22.0</td><td>30.0</td><td>30.3</td></tr>
  <tr><td>InterSP</td><td>36.5</td><td>37.6</td><td>41.8</td><td><b>15.8</b></td><td><b>15.8</b></td><td><b>15.8</b></td><td><b>52.8</b></td><td><b>71.6</b></td><td><b>77.1</b></td><td><b>37.3</b></td><td><b>41.5</b></td><td><b>44.0</b></td></tr>
  <tr>
    <td rowspan="15">Llama-3-8B</td>
    <td>Original</td><td>0.0</td><td>2.2</td><td>1.2</td><td>0.0</td><td>0.0</td><td>1.2</td><td>0.0</td><td>2.0</td><td>1.1</td><td>0.0</td><td>2.1</td><td>1.2</td>
  </tr>
  <tr><td>CPT</td><td>0.0</td><td>2.5</td><td>1.5</td><td>0.0</td><td>0.0</td><td>1.5</td><td>0.0</td><td>2.3</td><td>1.3</td><td>0.0</td><td>2.5</td><td>1.5</td></tr>
  <tr><td>CPT + SFT</td><td>19.3</td><td>21.9</td><td>25.1</td><td>6.8</td><td>6.8</td><td>6.8</td><td>36.1</td><td>55.1</td><td>24.3</td><td>20.4</td><td>26.0</td><td>28.0</td></tr>
  <tr><td>CPT + SFT (w/o F)</td><td>24.8</td><td>25.6</td><td>30.2</td><td>7.5</td><td>7.5</td><td>7.5</td><td>38.2</td><td>57.7</td><td>25.8</td><td>22.8</td><td>27.9</td><td>32.4</td></tr>
  <tr><td>MT</td><td>28.9</td><td>30.9</td><td>33.7</td><td>29.8</td><td>29.8</td><td>29.8</td><td>58.8</td><td>58.6</td><td>83.2</td><td>31.5</td><td>31.9</td><td>37.8</td></tr>
  <tr><td>MT (prompt)</td><td>28.8</td><td>31.2</td><td>34.3</td><td><b>30.6</b></td><td><b>30.6</b></td><td><b>30.6</b></td><td>59.1</td><td>59.1</td><td>84.9</td><td>31.7</td><td>32.6</td><td>38.9</td></tr>
  <tr><td>R&A</td><td>22.3</td><td>26.9</td><td>27.4</td><td>23.7</td><td>23.7</td><td>23.7</td><td>50.1</td><td>63.4</td><td>75.2</td><td>25.4</td><td>30.8</td><td>36.7</td></tr>
  <tr><td>Attn Drop</td><td>15.4</td><td>18.3</td><td>23.2</td><td>6.3</td><td>6.3</td><td>6.3</td><td>35.7</td><td>50.8</td><td>23.9</td><td>18.4</td><td>22.5</td><td>26.8</td></tr>
  <tr><td>D-AR</td><td>20.6</td><td>23.5</td><td>26.7</td><td>7.3</td><td>7.3</td><td>7.3</td><td>37.2</td><td>56.1</td><td>24.9</td><td>21.5</td><td>26.3</td><td>30.0</td></tr>
  <tr><td>PIT++</td><td>21.2</td><td>24.7</td><td>26.3</td><td>21.0</td><td>21.0</td><td>21.0</td><td>50.7</td><td>68.4</td><td>74.8</td><td>24.0</td><td>30.4</td><td>30.7</td></tr>
  <tr><td>InterSP</td><td><b>39.4</b></td><td><b>42.1</b></td><td><b>44.6</b></td><td>28.3</td><td>28.3</td><td>28.3</td><td><b>73.1</b></td><td><b>84.2</b></td><td><b>87.3</b></td><td><b>42.2</b></td><td><b>47.6</b></td><td><b>48.0</b></td></tr>
</table>

Table 1: Preliminary experimental results. Bold numbers denote the best results. Underline numbers imply the second-best results.

and the ground truth (Lin, 2004). During evaluation, for LLMs that have not been fine-tuned with instructions, five QA pairs are provided as in-context demonstrations that are used to ensure the output follows the specified format.

3.4 Experimental Results

As shown in Table 1, the relatively low knowledge QA performance of the original Llama-2 and Llama-3 (0% EM across all evaluations) indicates that all knowledge in the test set is not included in the original pre-training corpus. It can be seen that these methods are struggling to acquire novel knowledge, even though the perplexity has been reduced to 1 during the continued pre-training phase. Compared to only CPT, the EM after SFT increased to 21.2% and 20.4% for Llama-2 and Llama-3, respectively, indicating the effectiveness of the standard paradigm and that LLMs have captured some new knowledge. Notably, integrating pre-training and fine-tuning objectives shows superior performance compared to the separated CPT+SFT pipeline. Specifically, MT and MT (prompt) consistently outperform the standard separated training paradigm. This highlights the superiority of mixed training strategies over a sequential, distinctly separated training pipeline. Among the knowledge augmentation methods, InterSP achieved the best performance, achieving EM improvements of 0.2% and 10.5% for Llama-2 and Llama-3 over MT (prompt), respectively, highlighting the importance of sentence positioning within passages for effective knowledge learning. However, despite these improvements, current methods remain insufficient for enabling LLMs to master novel knowledge comprehensively.

4 Intra-sentence Permutation and PermAR Framework

Given the failures of various mainstream CPT and augmentation paradigms in new knowledge scenarios, we aim to further enhance LLM’s learning effectiveness of new knowledge. Drawn inspiration from (Golovneva et al., 2024; Guo et al., 2024), we conducted a position-based systematic analysis of CPT+SFT and InterSP to explore the relationship between the effectiveness of its learning by LLMs and the position of knowledge between and within sentences. Statistical results indicate both the position within and between sentences have a significant influence on prediction accuracy. Specifically, the average EM of the first three knowledge in the sentences that are positioned differently in passage of the test set is depicted in Figure 2 (a) and (c). It can be observed that for the same position within different sentences, the earlier a sentence appears in the passage, the more easily LLMs can learn that knowledge. Additionally, it can be
Figure 2: EM of CPT+SFT and InterSP on the test set using Llama-3-8B. (a) and (b) show the results for CPT+SFT, while (c) and (d) correspond to InterSP. (a) and (c) represent the EM scores of the \( i_{th} \) (\( i=1, 2, 3 \)) knowledge from different sentences, with the horizontal axis indicating the sentence index in passages. (b) and (d) categorize sentences based on their knowledge density, where G1-G6 represent groups with increasing numbers of knowledge points per sentence (G1: one knowledge point, G6: six knowledge points). The right vertical axis denotes the average position of sentences within the passage for each group.

observed that applying inter-sentence permutation enhances knowledge retention across different sentences. However, when observing Figure 2 (b), a distinct downstair-like pattern emerges within sentences meaning knowledge positioned earlier in a sentence is more easily retained, whereas knowledge appearing later remains significantly harder to grasp. Furthermore, Figure 2 (d) indicates that inter-sentence permutation alone is insufficient to bridge the accuracy gap between knowledge positioned earlier and later within the same sentence.

Therefore, to improve LLM’s ability to perceive knowledge at different positions within a sentence, a straightforward approach is to apply intra-sentence permutation alongside inter-sentence permutation (InterSP+IntraSP). Inspired by XLNet (Yang et al., 2019), InterSP+IntraSP can be viewed as a subset of permutation language modeling. Formally, the permutation language modeling objective can be expressed as an expectation over all possible factorization orders:

\[
\max_{\theta} \quad p_\theta(\mathbf{x}) = \mathbb{E}_{\tau \sim S_T} \left[ \prod_{t=1}^T p_\theta(x_{\tau_t} \mid x_{\tau_{<t}}) \right]
\]

where \( S_T \) denotes the symmetric group containing all \( T! \) permutations of the index set \( \{1, 2, \ldots, T\} \), and \( \tau = [\tau_1, \tau_2, \ldots, \tau_T] \) denotes a randomly sampled permutation from \( S_T \). Here, \( \tau_t \) indicates the token index at position \( t \) in the permuted order, while \( x_{\tau_{<t}} = [x_{\tau_1}, x_{\tau_2}, \ldots, x_{\tau_{t-1}}] \) encompasses all tokens preceding position \( t \) in the current permutation \( \tau \).

Since the same model parameters \( \theta \) are shared across all factorizations, each token \( x_i \) encounters diverse contextual configurations during training. This exposure enables the model to learn comprehensive token interdependencies, since across all permutations, every token \( x_i \) is eventually conditioned on every other token \( x_j, j \neq i \) and vice versa. Consequently, the model develops bidirectional understanding while maintaining the computational efficiency and causal structure inherent to AR modeling.

However, directly applying the permutation language modeling objective from Equation 2 to the standard AR model can fail in certain scenarios. For example, consider a sequence of length \( T \) corresponding to two different permutation orders \( \tau_a = [1, 2, 3, \cdots, T-1, T] \) and \( \tau_b = [1, 2, 3, \cdots, T, T-1] \). When predicting the second-to-last token, the standard AR model would return identical logits, despite the ground truth being different. The fundamental reason for this lies in the fact that the standard AR model, during next-token prediction, cannot incorporate the positional information of the target token.

Position-aware Instruction Embedding To enable a standard AR model to perceive differences in target positions when predicting the next token, we introduce a set of position-aware instruction embeddings to encode positional information of the next predicted token. We define a position-aware instruction embedding set \( \mathbf{p} = [p_1, p_2, \cdots, p_T] \in \mathbb{R}^{T \times dim} \), where \( p_i \) corresponds to the position-aware instruction embedding for predicting the token at position \( i \) and \( dim \) is the embedding dimension. These position embeddings are integrated with the input token embedding through element-wise addition, yielding the target position-aware token embedding \( \mathbf{x}_\tau \):

\[
\begin{align*}
\mathbf{x}_\tau^p &= \mathbf{x}_\tau + \mathbf{p}_\tau \\
&= [x_{\tau_1} + p_{\tau_2}, x_{\tau_2} + p_{\tau_3}, \cdots, x_{\tau_{T-1}} + p_{\tau_T}, x_{\tau_T}]
\end{align*}
\]
where \( \mathbf{x}_\tau \) represents the token embeddings of the original sequence after permutation \( \tau \), and \( \mathbf{p}_\tau \) represents the shifted position-aware instruction embeddings. Specifically, each token \( x_{\tau_t} \) receives the position-aware instruction embedding \( p_{\tau_{t+1}} \) corresponding to its next permuted token’s original position. The final token \( x_{\tau_T} \) receives no additional position-aware instruction embedding since no subsequent prediction is required.

For obtaining the position-aware instruction embedding, we design a parameter-efficient approach that learns a single shared embedding for all positions, \( \mathbf{e} \in \mathbb{R}^{1 \times dim} \), and applies rotational position encoding to incorporate positional information. Specifically, we leverage RoPE-ID (Su et al., 2024) to rotate \( \mathbf{e} \) according to the coordinate of the target prediction position. The position-aware instruction embedding for predicting a token at position \( t \) is computed as:

\[
p_t = \text{RoPE}(\mathbf{e}, t)
\]

This design offers computational efficiency by sharing parameters across all positions while maintaining distinct positional representations through rotation. Alternative implementations are feasible, including learning independent dense vectors for each position or employing trainable fusion mechanisms (\emph{e.g.}, learned linear combinations) in place of the additive operation in Equation 3. We provide comprehensive ablation studies comparing these design choices in Appendix C.4.

Permutation Annealing Strategy Although the proposed PermAR framework can be seamlessly integrated into existing AR models, enabling models to effectively learn new knowledge even when faced with small-scale knowledge, the number of possible permutations for a token sequence is exceedingly large. For instance, for a token sequence of length 1024, the number of possible permutations is 1024!, which would overwhelm the model and significantly reduce training efficiency. Meanwhile, based on the observation in Figure 2: standard AR models tend to better learn knowledge from earlier sentences of the passage, and even from earlier positions of the sentence.

Furthermore, we propose a permutation annealing strategy designed to help model reconstruct the fragmented knowledge learned during permutation training into more coherent and logically consistent knowledge, enabling a smooth transition from permutation to natural sequential order. Specifically, we introduce a dynamic probability \( r \in [0, 1] \) to control whether permutation is applied to the training samples, \emph{i.e.}, each sample is permuted with probability \( r \). When \( r = 1 \), the model undergoes full permutation training where token orders are completely permuted, while \( r = 0 \) corresponds to standard sequential training with preserved original token order. Formally, \( r \) can be modeled as follows:

\[
r = \text{clip}\left(1 - \frac{\text{epoch} - \text{start}}{\text{end} - \text{start}}, 0, 1\right)
\]

where \( \text{clip}(x, a, b) = \max(a, \min(x, b)) \), \( \text{epoch} \) denotes the current epoch during training, and \( \text{start} \) and \( \text{end} \) represent the beginning and ending epochs of the permutation annealing strategy, respectively.

5 Experiments

5.1 Settings

Baselines. We further compare knowledge augmentation methods with PermAR, including intra-sentence permutation (IntraSP) and the combination of inter-sentence and intra-sentence permutation (InterSP+IntraSP). Details of knowledge augmentation are shown in Appendix B.3 and B.4.

5.2 Main Results

The experimental results are shown in Table 2, from which we can draw the following conclusions: (1) Knowledge augmentation is essential for enabling LLMs to learn novel knowledge to some extent. Compared to the best non-augmented method, MT (prompt), the best augmentation-based method, InterSP+IntraSP, improves EM by 34.9%/22.2% (Llama-2/Llama-3), demonstrating the effectiveness of permutation patterns in enhancing knowledge learning. (2) A combination of InterSP and IntraSP is necessary, as neither alone is sufficient. InterSP+IntraSP outperforms single augmentation by 15.9%/5.8%. Additionally, IntraSP proves more critical for complex knowledge, surpassing InterSP alone by 18.8%/5.9%. (3) PermAR effectively mitigates conflicts between permutation-based knowledge augmentation and the AR objective, significantly improving knowledge comprehension at different positions. It enhances EM by 3.3%/9.8% compared to the best augmentation method.

Furthermore, we visualize the impact of knowledge augmentation and PermAR in Figure 3, revealing: (1) Knowledge augmentation methods strug-
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Method</th>
    <th colspan="3">Text</th>
    <th colspan="3">Number</th>
    <th colspan="3">Date</th>
    <th colspan="3">All</th>
  </tr>
  <tr>
    <th>EM</th><th>R</th><th>R-L</th>
    <th>EM</th><th>R</th><th>R-L</th>
    <th>EM</th><th>R</th><th>R-L</th>
    <th>EM</th><th>R</th><th>R-L</th>
  </tr>
  <tr>
    <td rowspan="4">Llama-2-7B</td>
    <td>InterSP</td>
    <td>36.5</td><td>37.6</td><td>41.8</td>
    <td>15.8</td><td>15.8</td><td>15.8</td>
    <td>52.8</td><td>71.6</td><td>77.1</td>
    <td>37.3</td><td>41.5</td><td>44.0</td>
  </tr>
  <tr>
    <td>IntraSP</td>
    <td>55.8</td><td>56.5</td><td>59.5</td>
    <td>36.5</td><td>36.5</td><td>36.5</td>
    <td>67.5</td><td>80.6</td><td>84.3</td>
    <td>56.1</td><td>59.2</td><td>60.8</td>
  </tr>
  <tr>
    <td>InterSP+IntraSP</td>
    <td>71.9</td><td>73.7</td><td>75.2</td>
    <td>45.4</td><td>45.4</td><td>45.4</td>
    <td>83.2</td><td>91.9</td><td>93.4</td>
    <td>72.0</td><td>75.8</td><td>75.8</td>
  </tr>
  <tr>
    <td>PermAR</td>
    <td><b>75.3</b></td><td><b>76.5</b></td><td><b>78.0</b></td>
    <td><b>54.3</b></td><td><b>54.3</b></td><td><b>54.3</b></td>
    <td><b>83.2</b></td><td><b>92.0</b></td><td><b>93.5</b></td>
    <td><b>75.3</b></td><td><b>78.3</b></td><td><b>78.6</b></td>
  </tr>
  <tr>
    <td rowspan="4">Llama-3-8B</td>
    <td>InterSP</td>
    <td>39.4</td><td>42.1</td><td>44.6</td>
    <td>28.3</td><td>28.3</td><td>28.3</td>
    <td>73.1</td><td>84.2</td><td>87.3</td>
    <td>42.2</td><td>47.6</td><td>48.0</td>
  </tr>
  <tr>
    <td>IntraSP</td>
    <td>44.4</td><td>57.2</td><td>55.2</td>
    <td>49.9</td><td>49.9</td><td>49.9</td>
    <td>81.5</td><td>93.1</td><td>94.5</td>
    <td>48.1</td><td>62.3</td><td>58.9</td>
  </tr>
  <tr>
    <td>InterSP+IntraSP</td>
    <td>51.3</td><td>61.6</td><td>60.2</td>
    <td>48.0</td><td>48.0</td><td>48.0</td>
    <td>80.2</td><td>92.2</td><td>93.8</td>
    <td>53.9</td><td>65.9</td><td>63.2</td>
  </tr>
  <tr>
    <td>PermAR</td>
    <td><b>61.7</b></td><td><b>69.2</b></td><td><b>68.0</b></td>
    <td><b>57.4</b></td><td><b>57.4</b></td><td><b>57.4</b></td>
    <td><b>84.4</b></td><td><b>93.8</b></td><td><b>95.1</b></td>
    <td><b>63.7</b></td><td><b>72.7</b></td><td><b>70.4</b></td>
  </tr>
</table>

Table 2: Comparison of QA performance (%) between knowledge augmentation and PermAR.

gle to bridge the accuracy gap between knowledge positioned earlier and later within passages and sentence, resulting in a downward trend observed in Figure 3 (a) and (c). However, as seen in Figure 3 (b) and (d), overall knowledge retention improves across positions. Despite this, knowledge located at earlier positions (loc1) is still learned more effectively. This is primarily due to the inherent conflict between permutation-based augmentation and the AR training objective, causing later knowledge in the sequence to be learned with greater difficulty. (2) PermAR overcomes this issue with position-aware embeddings, significantly improving knowledge acquisition across all positions. As shown in Figure 3 (e), knowledge at all positions exhibits a significant improvement. Notably, knowledge appearing at the end of the sequence (third knowledge in the 7th sentence) surpasses the retention of many earlier knowledge points. Additionally, Figure 3 (f) further illustrates the substantial improvement in learning for knowledge positioned later in the sequence, exhibiting an upward trend within each group.

Moreover, to verify the robustness of PermAR, we evaluate it on the Wiki2023 dataset in Appendix C.1, which was collected in previous work (Jiang et al., 2024b) and contains knowledge that is novel only for Llama-2.

5.3 Ablation Experiments

Different Permutation Granularities. While InterSP operates at the sentence level, IntraSP requires careful consideration of permutation granularity. We investigate three categories of permutation granularities: token-level, word-level, and multi-word phrase-level permutation. Each granularity presents distinct trade-offs between preserving semantic coherence and enabling positional learning flexibility. To systematically evaluate the impact of different permutation granularities on novel knowledge acquisition, we conduct comprehensive experiments on the NovelHuman dataset. The detailed results and analysis are presented in Table C2 of Appendix C.2. Our findings reveal that multi-word phrases serve as the optimal permutation granularity, achieving the balance between maintaining linguistic coherence and enabling robust positional knowledge learning. Multi-word phrase-level granularity preserves natural word dependencies while providing sufficient permutation diversity for model to learn position-invariant representations.

Permutation Annealing Strategy. To enable models to consolidate fragmented knowledge acquired during permutation training into coherent
representations, we introduce a permutation annealing strategy that gradually transitions from full permutation to natural sequential order. This three-stage approach is controlled by two hyper-parameters: start and end, defining the annealing period. The training process follows a progressive annealing schedule: initially, the model undergoes full permutation training to learn position-invariant knowledge representations; during the annealing phase (from start to end epochs), the permutation probability gradually decreases, allowing the model to begin integrating scattered knowledge fragments. Finally, the model focuses exclusively on natural sequential order to consolidate the learned knowledge into coherent representations. We systematically analyze the impact of different annealing schedules on model performance across the NovelHuman dataset. Detailed experimental results comparing various start and end configurations are presented in Table C3 of Appendix C.3. Our findings demonstrate that this progressive annealing strategy significantly enhances novel knowledge acquisition, with optimal performance achieved when models undergo substantial full permutation training followed by gradual transition and focused sequential consolidation. This three-stage approach effectively transforms dispersed knowledge fragments into comprehensive, coherent representations.

6 Related Work

To explore the mechanisms of LLMs learning novel knowledge, most works (Allen-Zhu and Li, 2024; Jiang et al., 2024b; Saito et al., 2025) first construct novel knowledge datasets and then train LLMs on this dataset by optimizing the organization of training data. For dataset construction, Allen-Zhu and Li built a dataset of human knowledge and tasks with six basic attributes. Jiang et al. built a dataset of film domains based on timestamps. However, these benchmarks are either too simple for LLMs or cannot ensure they have not appeared in the pre-training corpus of LLMs. It is worth noting that although counterfactual datasets have likely not been encountered during pre-training, they are completely contrary to reality and inherently lack rationality. Subsequently, Allen-Zhu and Li trained LLMs from scratch and found that standard AR does not enable LLMs to fully grasp new knowledge. To address this, they augmented the knowledge using techniques such as sentence shuffling and rewriting, allowing LLMs trained on the augmented dataset to successfully master most of the knowledge. However, this method failed on our more complex and diverse dataset NovelHuman. Meanwhile, some studies (Jiang et al., 2024b; Saito et al., 2025) explored the effects of AR training methods and data augmentation during the continual pretraining phase. Nevertheless, even with the most advanced training and data augmentation methods, LLMs’ ability to acquire new knowledge remains fundamentally limited.

7 Conclusion

We first propose a method for constructing novel knowledge datasets based on the theory of biological evolution and then synthetic a new dataset, called NovelHuman with humans as the subjects. Subsequently, we evaluate the impact of predominant CPT and SFT paradigms on NovelHuman. We find that LLMs exhibit not only inter-sentence sensitivity but also intra-sentence sensitivity. To address this issue, we propose a permutation modeling-based framework, PermAR, which can seamlessly integrate with existing AR models, endowing them with bidirectional learning capabilities and efficiently learning knowledge of different positions. Extensive experiments demonstrate the superiority of PermAR, providing insight for the future advancement of LLMs.

Limitations

Although we have proposed a knowledge synthesis method based on species evolution, it can be applied to a wide range of domains, such as mountains, rivers, and etc. Since the focus of this paper is on exploring the learning mechanisms of new knowledge in LLMs, we have only generated new knowledge for human entities, which are the most attribute-rich. Other domains have not been fully explored. Additionally, while our work primarily focuses on novel knowledge acquisition, the observed position sensitivity in knowledge learning is not confined to this specific scenario but rather represents a fundamental limitation inherent to autoregressive language models. The effectiveness of PermAR in addressing reasoning-intensive knowledge, math reasoning, or complex multi-hop reasoning tasks remains to be systematically validated in future investigations.
Acknowledgement

The authors would like to thank the anonymous reviewers for their insightful comments. This work was supported by National Science Foundation of China (Grant Nos.62376057), the Start-up Research Fund of Southeast University (RF1028623234), SEU Innovation Capability Enhancement Plan for Doctoral Students (No. CXJH_SEU 25132), BIT Research and Innovation Promoting Project (Grant No.2024YCYXY071), and the Big Data Computing Center of Southeast University.

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A  Novel Human Dataset

A.1  Novel Triple Construction

Data Source. We use the knowledge graph Wikidata\(^3\) as the data source and utilize the QLever\(^4\) SPARQL engine to extract all triples.

Ontology. For all subjects in Wikidata, we filter their corresponding ontology (instance of\(^5\)) to be "human", i.e., \( h_o^f = \) human.

Important Attribute. For human subjects, we set the important attribute to be the date of birth, i.e., \( r_f^i = \) date of birth.

Anchor Relation. Anchor relation is set to be the occupation, i.e., \( r_f^o = \) occupation. The basic idea is that the attribute expansion for groups of people with the same occupation is more reasonable.

Constraint Function. Constraint function RULE is that the date of birth of the subject to be expanded must be later than the date of birth of the newly generated subject.

In the practical construction process, we set the size of the parent set for expansion to a maximum of 10 subjects for attribute expansion. Additionally, when constructing unique names for new subjects, we take into account the specificity of human names. We use the tokenizer of GPT-4 to segment the names of the father and mother, then randomly select the first token of each word and randomly concatenate the remaining tokens to ensure that the generated names closely resemble real names.

A.2  Statistical Distribution

For the 8,507 new human subjects we constructed, we first generated a histogram of their relationships, as shown in Figure A1 (a). Then, as shown in Table A1 for each number of relationships corresponding to the subjects, we randomly selected 80% of the subjects as the training set and the remaining 20% as the testing set. Subsequently, we constructed the corresponding QA questions for both the training and testing sets.

Since we used GPT-4 in the process of constructing novel knowledge, its inherent knowledge bias tends to eliminate parts of our existing triples that are currently unreasonable with world knowledge.

For example, party X\(^6\) currently has no African members. After generating the novel knowledge, we analyzed the changes in the number of relations (amount of knowledge) in the generated knowledge, as shown by the green bars in Figure A1 (a). It can be seen that for new entities with more attributes, the generated knowledge does not reflect these attributes, mainly because GPT-4 has helped us eliminate some unreasonable aspects. Subsequently, we use the tokenizer of Llama-3-8B to tokenize the generated novel knowledge, and the results are shown in Figure A1 (b). Additionally, we tokenize generated questions, which are displayed in Figure A1 (c). Figure A1 (d) also shows the length of the answers. It can be seen that the answers are relatively concise and accurate, which is the reason we use EM as an evaluation metric.

B  Experimental Setting

B.1  Introduction of Baselines in Preliminary Experiment

CPT. CPT (Shi et al., 2025; Zhao et al., 2024) involves continuously training LLM on new data to update its parameters. This process helps LLMs to adapt to new information and maintain up-to-date knowledge. It focuses on minimizing the perplexity of passage to improve the model’s performance.

In our experiments, CPT is trained on all documents, including a mixture of training and test set documents. During evaluation, five demonstrations are provided to guide the model in following the answer format while responding to the QA from the test set.

CPT + SFT. Continued Pre-training + Supervised Fine-Tuning (CPT + IT) (Jiang et al., 2025; Siriwardhana et al., 2024) is a method designed to enhance LLMs’ capabilities by first updating its knowledge base through training on both existing training passage and new test passage (train knowledge + test knowledge), and then fine-tuning the model with instruction tuning using question-answer (QA) pairs, i.e., train QA. This approach ensures the LLM incorporates the latest information while reinforcing its foundational knowledge, and then focuses on improving its ability to follow specific instructions and respond accurately to queries.

CPT + SFT (w/o F). Continued Pre-training + Supervised Fine-Tuning without Forgetting (CPT +

\footnotetext{3https://dumps.wikimedia.org/wikidatawiki/entities}
\footnotetext{4https://github.com/ad-freiburg/qlever}
\footnotetext{5In wikidata, the instance of is used to denote the ontology to which it belongs.}
\footnotetext{6It's not a typo, it's a proxy.}
<table>
  <tr>
    <th>Data Split</th>
    <th>Number of novel knowledge</th>
    <th>Number of general question</th>
  </tr>
  <tr>
    <td>Train</td>
    <td>6,790</td>
    <td>115,019</td>
  </tr>
  <tr>
    <td>Test</td>
    <td>1,717</td>
    <td>29,202</td>
  </tr>
</table>

Table A1: Statistical results of the Novel Human dataset.

![Bar charts showing statistical results of the NovelHuman dataset, including changes in relation numbers owned by new human subjects after generating novel knowledge, token distribution of generated novel knowledge, token distribution of questions, and token distribution of answers.](page_340_384_1067_246.png)

Figure A1: Statistical results of the NovelHuman dataset. (a) denotes the change in relation numbers owned by new human subjects after generating novel knowledge. (b) means the token distribution of generated novel knowledge. (c) implies the token distribution of questions. (d) presents the token distribution of the answer.

IT (w/o forget)) (Kalajdzievski, 2024; Zhai et al., 2024) is a training paradigm where the model is first trained on both new passages (train knowledge) and test passages (test knowledge) to update its parameters with the latest information. After this phase, the LLM undergoes instruction tuning using question-answer (QA) pairs (train QA) while continuing to include test passages (test knowledge) in the training process. This approach ensures that the model retains its previously learned knowledge from the initial passage training phase while learning how to respond to specific queries through instruction tuning. By incorporating test passages during both training phases, the model continuously reinforces its understanding of the new information, preventing the loss of previously acquired knowledge and enhancing its ability to accurately respond to queries based on both old and new data.

MT. LLM is trained simultaneously on question-answer (QA) pairs, training passages (train knowledge), and new test passages (test knowledge) (Chen et al., 2024). This integrated training process allows the model to learn how to respond to specific queries through QA pairs while simultaneously updating its knowledge base with both existing and new information from the training and test documents.

MT (prompt). This variant of mixed training includes prompting alongside continued pre-training and instruction tuning. Prompts are used to guide the model’s learning process (i.e., In addition to outputs, prompt is also used to calculate losses), helping it to focus on relevant information and improving its ability to generate accurate and contextually appropriate responses.

Reading&Answering (referred to R&A). The Reading&Answering paradigm involves training the LLM by providing a passage followed by corresponding QA pairs (Lehmann et al., 2024). This method mimics a real-world scenario where the LLM reads a passage and then answers questions based on the information it has just read. It can be broken down into 1) Train & Test passage (Train knowledge): LLM is continued pre-trained on all passages. This phase simulates the human reading and learning stage of knowledge. 2) Train passages + Train QA + Test passages: Similar to MT, LLM is given a passage and related questions about the passage (which appear sequentially), and then the test passage is used for continued pre-training. This process simulates the scenario where a person reviews the book and then answers questions after reading (i.e., doing homework after a class).

Attn Drop. Attention Dropout (Hinton, 2012) (Attn Drop) follows the same process as CPT+SFT, with the difference being that during the pre-training phase, attention is randomly dropped in the self-attention module.

D-AR. D-AR (Saito et al., 2025) (Denoising Auto-Regressive Training) is a method that enhances knowledge extraction by introducing noise into the training data (You et al., 2022; Hao et al., 2024). It works by randomly replacing a certain percentage of token positions in the input with ran-
dom tokens, perturbing the model’s input. The training objective is then modified to focus on predicting the correct tokens while ignoring the corrupted ones. This approach encourages the model to learn to predict the next token under diverse conditions, promoting robust information extraction during testing. Essentially, D-AR improves the model’s performance by diversifying the input sequences, similar to how BERT uses token masking in its training.

PIT++. Jiang et al. (Jiang et al., 2024b) hypothesize that exposing LLMs to QA pairs before continued pre-training on passage is beneficial, as it allows the model to consider how knowledge from complex passages is obtained through questions during the encoding process. They proposed Pre-Instruction Tuning (PIT) and its variant PIT++ with the best performance, a method that guides and adjusts the questions before passage pre-training.

B.2 Preliminary Experiment Detail Setting

Hyperparameter. Typically, pre-training processes corpus data by concatenating all samples into a continuous sequence, with individual samples separated by a [SEP] token (Guo et al., 2025; Jiang et al., 2024a; Zheng et al., 2024). However, since our constructed NovelHuman dataset consists of relatively independent samples, we do not adopt the traditional concatenation approach. Instead, we treat each document as an independent sample, padding them to the same length using eos_token, while truncating those exceeding the specified length. In our experiments, during the continued pre-training phase, we set the maximum sequence length to 2048, with a per-GPU batch size of 8 and a total batch size of 64, full parameters fine-tuning using ZeRO-2 (Rasley et al., 2020) for optimization. We train with bf16 precision, an initial learning rate of \(1.0e - 4\), a warm-up ratio of 0.1, and a cosine scheduler, running for 150 epochs with an early stopping strategy. We use AdamW (Loshchilov and Hutter, 2018) with \( \beta_1 = 0.9, \beta_2 = 0.95 \), and a weight decay of 0.1. During continued pre-training, we evaluate perplexity (PPL) on the training set at each epoch and terminate training early if PPL drops below 2 and the change in PPL between consecutive epochs is \( \leq 0.1 \).

For supervised fine-tuning (SFT), our experiments show that full fine-tuning and LoRA fine-tuning yield similar performance. Given computational constraints, we adopt LoRA fine-tuning for all SFT stages. In our experiments, we set the rank size to 8, with a per-GPU batch size of 128 and a total batch size of 1,024. Training is conducted using bf16 precision, with an initial learning rate of \(8.0e - 5\), a warm-up ratio of 0.1, a cosine scheduler, and a total of 10 epochs.

<table>
  <tr>
    <th>Permutation</th>
    <th>Example</th>
  </tr>
  <tr>
    <td rowspan="2">Original</td>
    <td>Billabel Kinnamon was born on June 18, 1918, in Utica, United States of America. Kinnamon was educated at Bryn Mawr College...</td>
  </tr>
  <tr>
    <td>Kinnamon was educated at Bryn Mawr College. Billabel Kinnamon was born on June, 18, 1918, in Utica, United States of America...</td>
  </tr>
  <tr>
    <td>Intra-sentence</td>
    <td>1918, in Utica, Billabel Kinnamon was born on June 18, United States of America.</td>
  </tr>
</table>

Table B1: An example of permutation pattern, with \( k \) is set to 5.

B.3 PermAR Setting

In pre-training, we set the start epoch to 100 and the end epoch to 120. During intra-sentence permutation, three words are treated as the permutation granularity. Other experimental details remain consistent with those in Appendix B.2.

B.4 Knowledge Augmentation Setting

An instance of InterSP and IntraSP is shown in Table B1. Specifically, inter-sentence refers to the permutation of sentences within passages. Intra-sentence involves the permutation of words within a sentence. To maintain a certain level of semantic coherence, we ensure that up to \( k \) words are not permuted. For InterSP, each passage was permuted at the sentence level 20 times. For IntraSP, each sentence was permuted at the word level 20 times, while maintaining the original sentence order within the passage. For InterSP+IntraSP, passages were first permuted 4 times at the sentence level, and then each of the four shuffled passages underwent 5 rounds of word-level permutation within sentences, resulting in 20 permuted passages per person.

C Additional Experiments

C.1 Experiments on Wiki2023

To verify the robustness of PermAR on other datasets, we conduct experiments using Llama-2-7B on the Wiki2023 dataset (Jiang et al., 2024b).
<table>
  <tr>
    <th>Method</th>
    <th>EM</th>
    <th>R</th>
    <th>R-L</th>
  </tr>
  <tr>
    <td>PIT</td>
    <td>46.5</td>
    <td>52.3</td>
    <td>61.9</td>
  </tr>
  <tr>
    <td>PIT++</td>
    <td>48.1</td>
    <td>54.4</td>
    <td>66.4</td>
  </tr>
  <tr>
    <td>PermAR</td>
    <td><b>55.5</b></td>
    <td><b>63.8</b></td>
    <td><b>69.3</b></td>
  </tr>
</table>

Table C1: Comparison of QA performance between PermAR and PIT on the test set of the Wiki2023 dataset.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Granularity</th>
    <th colspan="3">InterSP+IntraSP</th>
    <th colspan="3">PermAR</th>
  </tr>
  <tr>
    <th>All</th>
    <th>EM</th>
    <th>R</th>
    <th>R-L</th>
    <th>All</th>
    <th>EM</th>
    <th>R</th>
    <th>R-L</th>
  </tr>
  <tr>
    <td rowspan="6">Llama2-7B</td>
    <td>Token</td>
    <td>30.7</td>
    <td>35.6</td>
    <td>41.3</td>
    <td>30.8</td>
    <td>37.6</td>
    <td>42.3</td>
  </tr>
  <tr>
    <td>Word</td>
    <td>66.8</td>
    <td>71.3</td>
    <td>72.5</td>
    <td>70.6</td>
    <td>72.2</td>
    <td>72.8</td>
  </tr>
  <tr>
    <td>M-Word</td>
    <td>2</td>
    <td>68.9</td>
    <td>72.8</td>
    <td>73.2</td>
    <td>73.3</td>
    <td>75.9</td>
    <td>76.2</td>
  </tr>
  <tr>
    <td></td>
    <td>3</td>
    <td>72.0</td>
    <td>75.8</td>
    <td>75.8</td>
    <td>75.3</td>
    <td>78.3</td>
    <td>78.6</td>
  </tr>
  <tr>
    <td></td>
    <td>4</td>
    <td>69.5</td>
    <td>73.4</td>
    <td>73.8</td>
    <td>74.4</td>
    <td>77.1</td>
    <td>77.9</td>
  </tr>
  <tr>
    <td></td>
    <td>5</td>
    <td>69.3</td>
    <td>73.1</td>
    <td>73.4</td>
    <td>73.5</td>
    <td>75.8</td>
    <td>76.1</td>
  </tr>
  <tr>
    <td rowspan="6">Llama3-8B</td>
    <td>Token</td>
    <td>29.6</td>
    <td>38.8</td>
    <td>40.2</td>
    <td>28.4</td>
    <td>37.2</td>
    <td>38.2</td>
  </tr>
  <tr>
    <td>Word</td>
    <td>51.0</td>
    <td>57.1</td>
    <td>56.1</td>
    <td>60.5</td>
    <td>70.9</td>
    <td>69.8</td>
  </tr>
  <tr>
    <td>M-Word</td>
    <td>2</td>
    <td>51.7</td>
    <td>57.9</td>
    <td>56.2</td>
    <td>61.6</td>
    <td>71.2</td>
    <td>70.3</td>
  </tr>
  <tr>
    <td></td>
    <td>3</td>
    <td>53.9</td>
    <td>65.9</td>
    <td>63.2</td>
    <td>63.7</td>
    <td>72.7</td>
    <td>70.4</td>
  </tr>
  <tr>
    <td></td>
    <td>4</td>
    <td>52.4</td>
    <td>58.5</td>
    <td>59.6</td>
    <td>62.5</td>
    <td>72.1</td>
    <td>70.0</td>
  </tr>
  <tr>
    <td></td>
    <td>5</td>
    <td>51.9</td>
    <td>58.2</td>
    <td>58.5</td>
    <td>61.1</td>
    <td>71.8</td>
    <td>69.3</td>
  </tr>
</table>

Table C2: Comparison of QA performance for different permutation granularities in knowledge acquisition, where M-word refers to multi-word.

The Wiki2023 dataset, proposed by Jiang et al., is a timestamp-based novel knowledge dataset designed to explore how LLMs acquire new knowledge. It primarily focuses on the film domain and is currently compatible only with the Llama-2 series models, making it unsuitable for other models. The experimental results are presented in Table C1. As seen in the table, PermAR demonstrates good robustness on Wiki2023, outperforming PIT++ by 7.4% in the EM metric.

C.2 Ablation Experiment on Different Permutation Granularities

From Table C2, we observe that token-level permutation performs the worst, primarily because LLMs do not always tokenize complete words as a single token. Instead, prefixes and suffixes are often split, leading to increased complexity when different prefixes and suffixes are recombined. This disrupts the model’s ability to extract meaningful patterns, forcing it to process disordered sequences, which negatively impacts knowledge learning.

In contrast, word-level permutation significantly improves model performance, indicating that LLMs can better learn novel knowledge at different positions when using complete word permutations. Furthermore, considering the intrinsic characteristics of natural language, many words form tightly connected phrases that should not be arbitrarily disrupted. To address this, we extend the permutation granularity from single words to multi-word phrases. Experimental results show that selecting an appropriate phrase length as the permutation granularity further enhances model performance.

It is worth noting that precisely determining which words exhibit strong interdependence falls beyond the scope of this study, and we leave this as a direction for future research.

C.3 Ablation Experiment on Permutation Annealing Strategy

We conducted experiments using Llama-3-8B on the NovelHuman dataset, and the results are shown in Table C3. The following observations can be made: (1) When \( start = 0 \) and \( end = 0 \), the model starts permutation annealing right from the beginning, and the experimental results are almost identical to those of CPT+SFT. (2) When \( start = 150 \) and \( end = 150 \), the model remains in the permutation phase throughout, without termination due to early stopping. This indicates that there are too many possible permutations for the model to learn within a limited time. Although there is a noticeable performance improvement compared to CPT+SFT, it is still lower than the optimal parameter configuration. This suggests that the model is learning scattered knowledge in various contexts without integrating it into a complete knowledge system. (3) When \( start = 100 \) and \( end = 120 \), the model achieves the best performance, demonstrating the advantage of extensive permutation learning followed by appropriate annealing train-

<table>
  <tr>
    <th>Start Epoch</th>
    <th>End Epoch</th>
    <th colspan="3">All</th>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>EM</td>
    <td>R</td>
    <td>R-L</td>
  </tr>
  <tr>
    <td>0</td>
    <td>0</td>
    <td>20.9</td>
    <td>26.8</td>
    <td>29.7</td>
  </tr>
  <tr>
    <td>0</td>
    <td>20</td>
    <td>25.9</td>
    <td>36.2</td>
    <td>38.8</td>
  </tr>
  <tr>
    <td>0</td>
    <td>50</td>
    <td>33.5</td>
    <td>39.9</td>
    <td>45.1</td>
  </tr>
  <tr>
    <td>0</td>
    <td>100</td>
    <td>39.8</td>
    <td>47.4</td>
    <td>53.0</td>
  </tr>
  <tr>
    <td>0</td>
    <td>150</td>
    <td>41.2</td>
    <td>47.8</td>
    <td>55.3</td>
  </tr>
  <tr>
    <td>50</td>
    <td>50</td>
    <td>53.4</td>
    <td>63.1</td>
    <td>66.3</td>
  </tr>
  <tr>
    <td>50</td>
    <td>100</td>
    <td>59.2</td>
    <td>66.8</td>
    <td>67.0</td>
  </tr>
  <tr>
    <td>50</td>
    <td>150</td>
    <td>60.2</td>
    <td>67.3</td>
    <td>68.9</td>
  </tr>
  <tr>
    <td>100</td>
    <td>100</td>
    <td>62.3</td>
    <td>71.2</td>
    <td>69.4</td>
  </tr>
  <tr>
    <td>100</td>
    <td>120</td>
    <td><b>63.7</b></td>
    <td><b>72.7</b></td>
    <td><b>70.4</b></td>
  </tr>
  <tr>
    <td>100</td>
    <td>150</td>
    <td>62.6</td>
    <td>71.9</td>
    <td>70.1</td>
  </tr>
  <tr>
    <td>120</td>
    <td>120</td>
    <td>61.0</td>
    <td>70.8</td>
    <td>68.9</td>
  </tr>
  <tr>
    <td>120</td>
    <td>140</td>
    <td>61.7</td>
    <td>71.5</td>
    <td>68.6</td>
  </tr>
  <tr>
    <td>150</td>
    <td>150</td>
    <td>61.4</td>
    <td>71.3</td>
    <td>68.2</td>
  </tr>
</table>

Table C3: Comparison of QA performance for different start and end epochs in permutation annealing training.
<table>
  <tr>
    <th>Position-aware Embedding</th>
    <th>Operation</th>
    <th>EM</th>
    <th>R</th>
    <th>R-L</th>
  </tr>
  <tr>
    <td rowspan="2">Dense</td>
    <td>add</td>
    <td>52.3</td>
    <td>55.8</td>
    <td>60.1</td>
  </tr>
  <tr>
    <td>merge</td>
    <td>48.3</td>
    <td>50.6</td>
    <td>53.7</td>
  </tr>
  <tr>
    <td>RoPE-1D</td>
    <td>add</td>
    <td><b>63.7</b></td>
    <td><b>72.7</b></td>
    <td><b>70.4</b></td>
  </tr>
</table>

Table C4: Comparison of QA performance of different position-aware embedding producing methods.

ing, and finally focusing on the original knowledge.

C.4 Ablation Experiment on Position-aware Instruction Embedding.

We learn a single embedding and obtain position-aware instruction embeddings for all positions using RoPE-1D, which are directly fused with token embeddings via an additive operation. Additionally, we explore two alternative approaches: learning a separate dense vector for each position and training a linear fusion layer to merge position-aware instruction embeddings with token embeddings. The experimental results are presented in Table C4.

Our findings show that the additive operation with RoPE-1D achieves the best performance. The main reason is that individually learning a dense vector for each position makes the model difficult to converge. Furthermore, RoPE-1D’s additive fusion efficiently scales to sequences of varying lengths, making it a more flexible and effective solution.

D Prompt Templates and Instances

Table D1 shows a prompt template for creating Wikipedia-style paragraphs from triples about individuals, including an example for Abelervéh Vill.

Table D2 provides a template for generating questions aimed at uncovering the object entity in a given triple. It includes detailed instructions and an example output format in JSON.

Table D3 and Table D4 show the prompt of checking the potential conflict or common sense violation of triples.

Table D5 describes a novel human subject, Paul Von Guillaume, from the Novel Human dataset. It details his biographical information, key achievements, and associated knowledge triples. It includes novel knowledge such as his birth, career, and personal life, and structured data like relations and questions pertaining to his life events.
Prompt Template for novel Knowledge Generation

Please convert the following collections of triples about individuals into detailed, cohesive paragraphs. Each paragraph should resemble the style of Wikipedia biographical entries, focusing on integrating the triples directly into the text. It’s crucial to incorporate each triple as it is presented, without paraphrasing or altering the original wording, and without drawing attention to any elements that might not be accurate. The narrative should flow naturally, engaging the reader with a formal tone and structured content akin to Wikipedia’s encyclopedic profiles. The triples cover various aspects of each person’s life, including personal background, achievements, relationships, and impact. Ensure that the integration of these triples into the paragraphs is seamless, maintaining the integrity of the original information.

Here are the triples:
{The list of triples}
Ensure the generated text is rich with detail, mimicking the depth and formal tone of a Wikipedia entry, to provide a thorough and engaging profile.

An instance of {the list of triples}
1.(Abelervéh Vill,award received,Maître d’art)2.(Abelervéh Vill,languages spoken, written or signed,Spanish)3.(Abelervéh Vill,social media followers,2471)4.(Abelervéh Vill,country of citizenship,France)5.(Abelervéh Vill,date of birth,1963-11-23 22:01:48)6.(Abelervéh Vill,place of birth,Pamplona)7.(Abelervéh Vill,Directory of Maîtres d’art,maitre-art/herve-obligi)8.(Abelervéh Vill,instrument,viola)9.(Abelervéh Vill,educated at,Hochschule für Musik Freiburg)10.(Abelervéh Vill,employer,Berlin University of the Arts)11.(Abelervéh Vill,field of work,visual arts)12.(Abelervéh Vill,student of,Harmut Rohde)13.(Abelervéh Vill,residence,New York City)14.(Abelervéh Vill,place of death,Boulogne-Billancourt)15.(Abelervéh Vill,sibling,Julian Grosvenor, Viscount Grey de Wilton)16.(Abelervéh Vill,social classification,nobility)17.(Abelervéh Vill,native language,French)18.(Abelervéh Vill,student,Pierre Lénert)19.(Abelervéh Vill,record label,Virgin Music)20.(Abelervéh Vill,copyright status as a creator,copyrights on works have expired)21.(Abelervéh Vill,has works in the collection,Metropolitan Museum of Art)22.(Abelervéh Vill,work location,Prague)

Table D1: Prompt template for novel knowledge generation and the corresponding instance.

Prompt Template of General Question Generation

I’m looking forward to generating at least five question templates specifically designed to uncover information about the ‘object’ entity within a given triple of (subject, relation, object). Each question template should effectively probe for details that lead to the ‘object’ as an answer. For this task, I will provide the ‘subject’, the ‘relation’, and a description of the relation to help you understand the relationship between subject and object.

Here are the details:
Subject:
{subject}
Relation:
{relation}
Relation Description:
{description}
Objectives:
1. Direct Information Retrieval: Each interrogative question is carefully crafted to directly solicit the entire "object" entity without intermediate steps or answers that are not the entire "object" itself.
2. Clarity and Precision: Ensure the questions are clear, concise, and precisely targeted at uncovering the whole 'object' entity based on the given 'relation' and its description.
3. Ensure that the question is an interrogative sentence, while avoiding types of questions such as Could, Can, Does, Do.
4. Questions can only consist of subjects, relations, and at most descriptions of relations.
5. Produce your output as JSON. The format should be:
{
    "question1": "Where was [T] born?",
    "question2": "What is the birth location of [T]?",
}

Table D2: Prompt template of general question generation for novel knowledge.
Prompt Template of Triple-checking
I have provided a list of triples (subject, predicate, object) concerning the attributes of a "Novel Human." The data you receive pertains to the attributes of this hypothetical entity, and it is important to identify any contradictions between these attribute values or any violations of common sense (e.g., values that are unrealistic or cannot logically coexist with each other).
Please carefully review the given triples and determine if any of the attribute values contradict each other, or if any values deviate significantly from what would be expected for a human in reality. This includes checking for conflicts or inconsistencies within the same set of triples and ensuring that the provided information aligns with what is commonly understood about humans.
If contradictions or unrealistic values are identified, please make the necessary corrections. If a triple is fundamentally inconsistent with other triples or violates common sense, mark it for deletion.
The provided triples are as follows:
{The list of triples}
Instructions:
Identity Contradictions: Check for contradictions between the attribute values of the "Novel Human." For example:
Conflicting birth or death dates.
Inconsistent or impossible combinations of attributes (e.g., a person listed as both alive and dead).
Identify Common Sense Violations: Check for attribute values that are unrealistic or violate common sense, such as: An age that is not plausible (e.g., someone born in 1800 but participating in modern activities). Attributes related to height, weight, or achievements that would be physically or logically impossible.
Modify: Adjust any attribute values that are unrealistic, illogical, or contradict the context of the "Novel Human."
Delete: Remove any triples that cannot logically coexist with other provided data, violate common sense, or are inconsistent with the characteristics of the "Novel Human."
Output Format:
The response should consist of one single JSON object, containing all modifications or deletions.
The key in the JSON object should be the original triple, and the value should be either:
The modified triple (if the triple needs to be adjusted to correct contradictions or violations).
"Delete" (if the triple should be removed due to contradictions or common sense violations).
Here's the structure of the JSON file you should output:
{
    "(original triple 1)": "(modified triple 1)",
    "(original triple 2)": "(modified triple 2)",
    "(original triple 3)": "Delete",
    ...
}

Table D3: Prompt template of checking the potential conflict or common sense violation of triples.
Prompt Example of Triple-checking

I have provided a list of triples (subject, predicate, object) concerning the attributes of a "Novel Human." The data you receive pertains to the attributes of this hypothetical entity, and it is important to identify any contradictions between these attribute values or any violations of common sense (e.g., values that are unrealistic or cannot logically coexist with each other).
Please carefully review the given triples and determine if any of the attribute values contradict each other, or if any values deviate significantly from what would be expected for a human in reality. This includes checking for conflicts or inconsistencies within the same set of triples and ensuring that the provided information aligns with what is commonly understood about humans.
If contradictions or unrealistic values are identified, please make the necessary corrections. If a triple is fundamentally inconsistent with other triples or violates common sense, mark it for deletion.
The provided triples are as follows:
1.(Paul Von Guillaume,place of birth,Cologne) 2.(Paul Von Guillaume,date of death,1963-11-25 00:17:56) 3.(Paul Von Guillaume,sport,auto racing) 4.(Paul Von Guillaume,award received,National Inventors Hall of Fame) 5.(Paul Von Guillaume,place of death,Montecagle) 6.(Paul Von Guillaume,languages spoken, written or signed,German) 7.(Paul Von Guillaume,participant in,24 Hours of Le Mans) 8.(Paul Von Guillaume,country of citizenship,United States of America) 9.(Paul Von Guillaume,date of birth,1874-06-15 07:12:58) 10.(Paul Von Guillaume,educated at,Northwestern University) 11.(Paul Von Guillaume,position held,Alderman of Corporation of the City of Adelaide) 12.(Paul Von Guillaume,residence,North Adelaide) 13.(Paul Von Guillaume,writing language,English) 14.(Paul Von Guillaume,copyright status as a creator,works protected by copyrights) 15.(Paul Von Guillaume,employer,Bonanza Air Lines) 16.(Paul Von Guillaume,place of burial,Memory Gardens Memorial Park) 17.(Paul Von Guillaume,height,316) 18.(Paul Von Guillaume,mass,100) 19.(Paul Von Guillaume,social media followers,130605) 20.(Paul Von Guillaume,different from,Joan Hubbard Wolf) 21.(Paul Von Guillaume,member of political party,National Fascist Party) 22.(Paul Von Guillaume,number of children,4) 23.(Paul Von Guillaume,sibling,Jim Hubbard) 24.(Paul Von Guillaume,owner of,I.H.Farm) 25.(Paul Von Guillaume,stepparent,Pavel Tykač) 26.(Paul Von Guillaume,native language,Portuguese) 27.(Paul Von Guillaume,pseudonym,Jojo la Moto) 28.(Paul Von Guillaume,member of sports team,Bernhard-Webber-Hartley) 29.(Paul Von Guillaume,number of matches played/races(starts),44) 30.(Paul Von Guillaume,number of wins,0) 31.(Paul Von Guillaume,podium finishes,1) 32.(Paul Von Guillaume,points for,5) 33.(Paul Von Guillaume,pole positions,0) 34.(Paul Von Guillaume,sponsor,Red Bull GmbH) 35.(Paul Von Guillaume,country for sport,Italy)

Instructions:
Identify Contradictions: Check for contradictions between the attribute values of the "Novel Human." For example:
Conflicting birth or death dates.
Inconsistent or impossible combinations of attributes (e.g., a person listed as both alive and dead).
Identify Common Sense Violations: Check for attribute values that are unrealistic or violate common sense, such as: An age that is not plausible (e.g., someone born in 1800 but participating in modern activities). Attributes related to height, weight, or achievements that would be physically or logically impossible.
Modify: Adjust any attribute values that are unrealistic, illogical, or contradict the context of the "Novel Human."
Delete: Remove any triples that cannot logically coexist with other provided data, violate common sense, or are inconsistent with the characteristics of the "Novel Human."
Output Format:
The response should consist of one single JSON object, containing all modifications or deletions.
The key in the JSON object should be the original triple, and the value should be either:
The modified triple (if the triple needs to be adjusted to correct contradictions or violations).
"Delete" (if the triple should be removed due to contradictions or common sense violations).
Here's the structure of the JSON file you should output:

{
    "(original triple 1)": "(modified triple 1)",
    "(original triple 2)": "(modified triple 2)",
    "(original triple 3)": "Delete",
    ...
}

Response of GPT-4

{
    "(Paul Von Guillaume, height, 316)": "(Paul Von Guillaume, height, 176)",
    "(Paul Von Guillaume, mass, 100)": "(Paul Von Guillaume, mass, 76)",
    "(Paul Von Guillaume, place of birth, Cologne)": "(Paul Von Guillaume, place of birth, Germany)",
    "(Paul Von Guillaume, place of death, Montecagle)": "(Paul Von Guillaume, place of death, United States of America)",
    "(Paul Von Guillaume, languages spoken, written or signed, German)": "Delete",
    "(Paul Von Guillaume, member of political party, National Fascist Party)": "Delete",
    "(Paul Von Guillaume, sponsor, Red Bull GmbH)": "Delete",
    "(Paul Von Guillaume, country for sport, Italy)": "Delete"
}

Table D4: Prompt example of triple checking and the response of GPT-4.
Table D5: A specific novel human subject in NovelHuman dataset.

<table>
  <tr>
    <th>Name</th>
    <th>A Novel Person</th>
  </tr>
  <tr>
    <td>Paul Von Guillaume</td>
    <td>
      1.(Paul Von Guillaume,place of birth,Germany) 2.(Paul Von Guillaume,date of death,1963-11-25 00:17:56) 3.(Paul Von Guillaume, sport,auto racing) 4.(Paul Von Guillaume,award received,National Inventors Hall of Fame) 5.(Paul Von Guillaume,place of death,United States of America) 6.(Paul Von Guillaume,participant in,24 Hours of Le Mans) 7.(Paul Von Guillaume,country of citizenship,United States of America) 8.(Paul Von Guillaume,date of birth,1874-06-15 07:12:58) 9.(Paul Von Guillaume,educated at,Northwestern University) 10.(Paul Von Guillaume,position held,Alderman of Corporation of the City of Adelaide) 11.(Paul Von Guillaume,residence,North Adelaide) 12.(Paul Von Guillaume,writing language,English) 13.(Paul Von Guillaume,copyright status as a creator,works protected by copyrights) 14.(Paul Von Guillaume,employer,Bonanza Air Lines) 15.(Paul Von Guillaume,place of burial,Memory Gardens Memorial Park) 16.(Paul Von Guillaume,height,176) 17.(Paul Von Guillaume,mass,76) 18.(Paul Von Guillaume,social media followers,130605) 19.(Paul Von Guillaume,different from,Joan Hubbard Wolf) 20.(Paul Von Guillaume,number of children,4) 21.(Paul Von Guillaume,sibling,Jim Hubbard) 22.(Paul Von Guillaume,owner of,I.H.Farm) 23.(Paul Von Guillaume,stepparent,Pavel Tykač) 24.(Paul Von Guillaume,native language,Portuguese) 25.(Paul Von Guillaume,pseudonym,Jojo la Moto) 26.(Paul Von Guillaume,member of sports team,Bernhard-Webber-Hartley) 27.(Paul Von Guillaume,number of matches played/races/starts,44) 28.(Paul Von Guillaume,number of wins,0) 29.(Paul Von Guillaume,podium finishes,1) 30.(Paul Von Guillaume,points for,5) 31.(Paul Von Guillaume,pole positions,0)
    </td>
  </tr>
</table>

Continued on next page
<table>
  <tr>
    <th>Generated knowledge</th>
    <td>Paul Von Guillaume (born June 15, 1874 – November 25, 1963) was a German-born American auto racing driver, engineer, and public official. Over the course of his diverse career, he made significant contributions to both the automotive and aviation industries, while also serving in municipal government. He was recognized for his innovations with an induction into the National Inventors Hall of Fame. Von Guillaume was born in Germany and later became a citizen of the United States of America. He pursued higher education at Northwestern University, which laid the foundation for his varied professional endeavors. He established his residence in North Adelaide, where he became actively involved in public service, holding the position of Alderman of the Corporation of the City of Adelaide. His professional career extended into the aviation sector, where he worked for Bonanza Air Lines. In addition to his technical and administrative pursuits, Von Guillaume had a deep passion for auto racing. He was a participant in the prestigious 24 Hours of Le Mans and competed as a member of the Bernhard-Webber-Hartley racing team. Over his career, he took part in 44 races, achieving one podium finish and earning five points in competitive racing. Despite his dedication to the sport, he did not secure any victories or pole positions. His height and weight were recorded as 176 cm and 76 kg, respectively. He was widely known within the racing community by his pseudonym, Jojo la Moto. Beyond his career in motorsports and governance, Von Guillaume was also an individual of cultural and intellectual depth. His native language was Portuguese, though he was proficient in English, which he used for his writing. His creative works remained protected by copyrights, ensuring their intellectual integrity. He was the owner of I.H. Farm, suggesting an involvement in agriculture or rural affairs. Von Guillaume maintained a complex personal life. He had four children and was a sibling to Jim Hubbard. His stepparent was Pavel Tykač, though details regarding their relationship remain limited. He was noted to be distinct from Joan Hubbard Wolf, possibly in response to historical misidentifications. At the time of his passing, Von Guillaume resided in the United States, where he died on November 25, 1963. He was laid to rest at Memory Gardens Memorial Park. Despite his passing, his legacy in engineering, motorsports, and civic leadership endures, marked by both his professional achievements and his significant social media presence, where he had 130,605 followers.</td>
  </tr>
  <tr>
    <th>Relation and the corresponding QA</th>
    <td>"place of birth":<br>{<br>  "value": "Germany",<br>  "questions":<br>    [<br>      ["What is the most specific known birth location of Paul Von Guillaume?", "Germany"]<br>      ...<br>    ]<br>  }<br>...<br></td>
  </tr>
</table>

Reviewer paper summaries: This paper addresses the challenge of integrating novel knowledge into Large Language Models (LLMs), particularly focusing on overcoming limitations in existing dataset construction methods and positional biases during training. The authors propose NovelHuman, a synthetic dataset generated via evolutionary operations (inheritance, mutation, expansion) on knowledge graphs to simulate realistic, unseen entities. They identify intra-sentence positional sensitivity in LLMs, where knowledge appearing later in sentences is poorly retained. To mitigate this, they introduce PermAR, a permutation-based autoregressive framework that integrates position-aware embeddings and a permutation annealing strategy. PermAR enables bidirectional context learning while maintaining compatibility with standard AR architectures. Experiments on Llama-2 and Llama-3 demonstrate PermAR’s superiority, outperforming existing methods by 3.3%–38% in exact match (EM) metrics. The work highlights the importance of dataset quality and positional awareness in knowledge acquisition.
Summary of strengths: The paper’s primary strengths is its innovative approach to constructing a novel knowledge dataset. Drawing inspiration from biological evolution to generate synthetic entities and diverse attribute combinations is both original and conceptually robust. This method provides a fresh perspective on the longstanding problem of updating LLMs with new information. Additionally, the PermAR framework is elegantly designed; by introducing minimal modifications to existing autoregressive models via position-aware embeddings, it bridges the gap between traditional left-to-right processing and the need for bidirectional context in learning new knowledge. The experimental results are compelling, with extensive ablation studies and comparisons against a range of baselines that highlight the superiority of the proposed methods. The detailed analysis of intra-sentence versus inter-sentence sensitivity offers valuable insights that could guide future research in LLM training strategies. Overall, the clear presentation of empirical improvements and systematic exploration of permutation strategies stands out as a major contribution to the field.
Summary of weaknesses: 1. The NovelHuman dataset is exclusively focused on human entities, limiting the empirical validation of the method's applicability to other domains. While the approach may be theoretically adaptable to non-fact-based or more diverse knowledge domains such as geography, the absence of experiments beyond human entities raises concerns about its broader generalizability.
2. Evaluations are confined to synthetic datasets like NovelHuman and Wiki2023, with no experiments on established factual QA benchmarks such as TriviaQA, which limits the ability to fully contextualize PermAR’s versatility.
3. Permutation-based training, which can involve up to 1024! permutations, likely incurs significant computational overhead that is not quantified or discussed, and the reliance on specific start and end epoch settings in the permutation annealing strategy has not been evaluated across diverse datasets, raising practical deployment concerns.
Comments, suggestions and typos: 1. To strengthen the work, the authors could extend evaluations to non-human domains (e.g., synthetic geographical entities) and compare PermAR with non-AR architectures (e.g., T5 and BERT) to clarify its bidirectional learning advantages.
2. Clarifying how position-aware embeddings interact with pre-trained positional encodings and providing concrete examples of intra-sentence permutations (e.g., token vs. phrase shuffling) would enhance technical transparency.
Overall assessment scores: 4
Metareview score: 4
Metareview: Assessing how well large language models acquire new information is a difficult problem, as the pretraining corpus may very well have included this information. This paper presents a method for generating synthetic knowledge graphs with an evolutionary algorithm, and generating synthetic sentences representing facts within that knowledge graph. As these facts are purely synthetic, they are unlikely to overlap with existing LLM training sets. They use a synthetic knowledge graph of people to evaluate how well existing LLMs can be tuned to incorporate new knowledge, evaluated on a synthetic question-answering task. The paper also presents a technique for learning autoregressive LLMs that are more robust to word movement, PermAR. PermAR involves permuting the words within finetuning sentences, but also permuting the position indices likewise. This yields models that are more robust to word movement.
Summary of reasons to publish: All reviewers appreciated PermAR, and believed this has the potential to be adopted more widely in LLM finetuning. Reviewers agreed that the paper was written clearly in general, and generating synthetic knowledge bases (for evaluation purposes) was well-motivated given the span of pretraining corpora. All reviewers agreed the experiments and motivation for this paper is sound, and found the evolutionary algorithm to generate synthetic knowledge bases to be interesting.