Conflict-Aware Knowledge Editing in the Wild: Semantic-Augmented Graph Representation for Unstructured Text
Authors: Zhange Zhang
Zhicheng Geng
Yuqing Ma
Tianbo Wang
Kai Lv
Xianglong Liu
Venue: NeurIPS 2025 spotlight ()
Keywords: Large Language Models
Model Editing
Unstructured Text
Conflict-Aware
Primary area: deep_learning

Abstract: Large Language Models (LLMs) have demonstrated broad applications but suffer from issues like hallucinations, erroneous outputs and outdated knowledge. Model editing emerges as an effective solution to refine knowledge in LLMs, yet existing methods typically depend on structured knowledge representations.  
However, real-world knowledge is primarily embedded within complex, unstructured text. Existing structured knowledge editing approaches face significant challenges when handling the entangled and intricate knowledge present in unstructured text, resulting in issues such as representation ambiguity and editing conflicts. 
To address these challenges, we propose a Conflict-Aware Knowledge Editing in the Wild (CAKE) framework, the first framework explicitly designed for editing knowledge extracted from wild unstructured text. 
CAKE comprises two core components: a Semantic-augmented Graph Representation module and a Conflict-aware Knowledge Editing strategy. The Semantic-augmented Graph Representation module enhances knowledge encoding through structural disambiguation, relational enrichment, and semantic diversification. Meanwhile, the Conflict-aware Knowledge Editing strategy utilizes a graph-theoretic coloring algorithm to disentangle conflicted edits by allocating them to orthogonal parameter subspaces, thereby effectively mitigating editing conflicts. Experimental results on the AKEW benchmark demonstrate that CAKE significantly outperforms existing methods, achieving a 15.43\% improvement in accuracy on llama3 editing tasks. Our framework successfully bridges the gap between unstructured textual knowledge and reliable model editing, enabling more robust and scalable updates for practical LLM applications.

Conflict-Aware Knowledge Editing in the Wild:
Semantic-Augmented Graph Representation for Unstructured Text
Zhange Zhangl223. Zhicheng Gengl.2 Yuqing Ma Tianbo Wang2. Kai Lv Xianglong Li Institute of Artificial Intelligence, Beihang University!
State Key Laboratory of Complex & Critical Software Environment, Beihang University
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing"
School of Computer Science and Engineering, Beihang University", Beijing Jiaotong University {zhangezhang wew.aauaannnguuaa...aaaaa edu.cn
Abstract
Large Language Models (LLMs) have demonstrated broad applications but suf- fer from issues like hallucinations, erroneous outputs and outdated knowledge. Model editing emerges as an effective solution to refine knowledge in LLMs, yet existing methods typically depend on structured knowledge representations. However, real-world knowledge is primarily embedded within complex, unstruc- tured text. Existing structured knowledge editing approaches face significant challenges when handling the entangled and intricate knowledge present in un- structured text, resulting issues such as representation ambiguity and editing conflicts. To address these challenges, we propose Conflict- Knowledge Editing in the Wild (CAKE) framework, the first framework explicitly designed for editing knowledge extracted from wild unstructured text. CAKE comprises two core components: Semantic-augmented Graph Representation module and Conflict-aware Knowledge Editing strategy. The Semantic-augmenter Graph Representation module enhances knowledge encoding through structural disam- biguation, relational enrichment, and semantic diversification. Mcanwhile, the Conffict-aware Knowledge Editing strategy utilizes a graph-theoreth coloring al- gorithm to disentangle conflicted edits by allocating them orthogonal parameter subspaces, thereby effectively mitigating editing conflicts. Experimental results the AKEW benchmark demonstrate that CAKE significantly outperforms isting methods, achieving a 15.43% improvement in accuracy on Ilama3 editing tasks. Our framework successfully bridges the gap between unstructured textual knowledge and reliable model editing, enabling more robust and scalable updates for practical LLM applications.
Introduction
Large Language Models (LLMs) exhibit remarkable knowledge retention capabilities and have been widely deployed in applications such conversational agents 2 medical diagnosis (3) and code generation [4]. Nevertheless, several fundamental challenges persist in current LLMs, including hallucination, erroneous outputs, and outdated knowledge [S] 7 To address these limitations, model editing has emerged as promising approach to precisely update targeted knowledge while preserving the model's general capabilities.
"Equal contribution
1 Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Unstructured Text
Multi-hop Query:
Overlopping Subject
Distinct Editing Target
Who the sponsor the Hannah Rarnes rides for?
Uno-X Pro Cyeling
eyeling team
Team is
Norwegiam
Hannah
the Uno-X Pro
Uno-X
Uno-X Cyeling Team
eycling toam.
profossional Women's
sponsored by Uno-
The team
Homes
rides for
2-hop reasoning
Cycling Team
sponsored
Seowad Rap
Uno-X Pro Cycling Team
is sponsored
by
Uno-X
British racing
Dlverse Semantic Query:
cyclisi Hannah
Which professional team does Haannhh
Barnes is contracted to
Pro Cyeling Team
rides the Uno-X
Hznnah
Harnes
4g Diverse
rides for
dhe UniXX
Cycling
conflicting grodient
Contraolctory
Updates
Complex Editing
Queries
Representation Ambiguity
Editing Conflicts
Figure I: Challenges of WUKE. (1) Representation Ambiguity: The intrinsic complexity infor-
mational interdependencies generates significant uncertainty in knowledge encoding. (2) Editing Conflicts: While structured editing operates on independent samples with low correlation, unstruc- tured text exhibits dense semantic overlaps.
Existing model editing methods predominantly rely on structured knowledge representations, such as triples (subject, relation, object) or Question-Answer (QA) pairs. For example, ROME X leverages causal mediation analysis over knowledge triples to identify editable regions in Transformer layers, subsequently updating parameters via key-value modifications. MEMIT [9] extends this framework enable batch editing of multiple triples. In contrast, GRACE (TO) maintains discrete codebook as external memory to track edited knowledge. Recent work like WISE 7 introduces memory architecture guided by knowledge triples to isolate edits, while AnyEdit [[] employs recursive optimization based on QA pairs to handle variable-length knowledge formats.
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However, real-world knowledge primarily stored in unstructured text (e.g., documents, articles) without explicit question-answer pairs, and transforming such data into structured formats requires costly human annotation, posing significant challenges for knowledge editing in practical applications. To address this, [12] proposed the Wild Unstructured Knowledge Editing (WUKE) paradigm. which aims to extract knowledge from complex unstructured text update LLMs. Building on WUKE, [12 introduced the AKEW benchmark, which uses raw text passages exclusively for training editing models, while employing knowledge-intensive QA pairs for evaluation. A substantial semantic gap exists between test queries and the edited knowledge-sun such as semantic paraphrases or multi- hop reasoning over multiple facts-adding adding difficulty to alignment and evaluation. [22 leveraged large language models to extract knowledge triples from unstructured text, thereby enabling the application of existing structured knowledge editing techniques like ROME(8] Empirical findings reveal substantial performance deterioration in current methodologies when processing authentic unstructured text, highlighting a crucial unresolved challenge knowledge editing research.
As illustrated in Figure 2 wild unstructured text manifests complex knowledge entanglement, with real-world queries frequently displaying substantial semantic deviations from source passages, posing persistent challenges for WUKE: (1) Representation Ambiguity: The intrinsic complexity of informational interdependencies generates significant uncertainty in knowledge encoding. Contrasted with semantically explicit structured triples, unstructured text interlinks diverse entities through intricate relational networks where single entity may associate with multiple counterparts via distinct relations. Moreover, entities can implicitly connect through transitive relational pathways without direct relations. This complexity is further exacerbated in evaluation scenarios with diverse real- world queries which require robust understanding of unstructured contexts. (2) Editing Conflicts: While structured editing operates on independent samples with low correlation, unstructured text exhibits dense semantic overlaps (e.g., shared entities/similar relations). The autoregressive training paradigm exacerbates this issue: concurrent editing of semantically adjacent yet cettiiitionnoofcttinng statements (e.g., "Paris is in France" versus "Parisian culture emphasizes fashion") induces adversarial gradient directions during parameter optimization. This mechanistic conflict corroborates prior research [3] [4] editing identical subject entities, where opposing objectives destabilize model convergence, ultimately degrading performance through competing parameter updates.
In this work, we propose Conflict-Aware Knowledge Editing in the Wild (CAKE) framework for wild unstructured text, which comprises Semantic-augmented Graph Representation (SGR) and Conflict-awarr Knowledge Editing strategy (CKE). To address knowledge representation ambi-

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Figure 2: An overview of CAKE. The SGR module first enhances knowledge semantics through three synergistic mechanisms: structural disambiguation, relational enrichment, and semantic diversifica- tion. The CKE module introduces a graph-theoretic coloring mechanism that decouples semantically overlapping edits into orthogonal parameter subspaces eliminate the editing conflicts.
guity. SGR systematically enhances knowledge semantics through three synergistic mechanisms: (1) Structural Disambiguation via dynamic knowledge graph construction from extracted triples, explicitly modeling complex entity interdependencies; (2) Relational Enrichment using multi-hop reasoning capture latent multidimensional associations; (3) Semantic Diversification through relation paraphrasing that generalizes context-aware predicate variations. This tripartite approach ensures robust encoding of unstructured knowledge while preserving inherent complexity. Further- more, to mitigate semantic interference, our CKE introduces a graph-theoretic coloring mechanism that decouples semantically overlapping edits into orthogonal parameter subspaces. Building on first construct a conflict graph quantifying
semantic- augmented knowledge graph embeddings, pairwise editing conflicts through subject entity sharing and relational similarity. Conflicting edits are then allocated to mutually orthogonal subspaces via classical graph coloring, effectively neutralizing gradient interference between entangled knowledge units during optimization.
Our contributions are as follows:
We propose Conflict- Awarr Knowledge Editing, the first knowledge editing framework specif- ically designed for unstructured text. This framework effectively addresses the challenges of representation ambiguity and Editing conflicts in WUKE, facilitating the application of knowledge editing in real-world scenarios.
We develop a Semantic Semantic-augmented Graph Representation module (SGR) that enhances knowledge semantics through three synergistic mechanisms: structural disambiguation, relational enrichment, and semantic diversification to address the knowledge ambiguity.
We introduce the Conflict-aware Knowledge Editing strategy (CKE), which introduces a graph- theoretic coloring mechanism that decouples semantically overlapping edits into orthogonal parameter subspaces to eliminate the editing conflicts.
Extensive experiments conducted on the standard WUKE benchmark demonstrate the effective- ness of our method. In particular, our approach achieves a 15.43% improvement in accuracy over the second-best method on editing Ilama3.
2 Method
Prior knowledge editing methods primarily rely on structured samples and encounter significant challenges when applied to unstructured text due to the inherent complexity of knowledge. These challenges manifest as representation ambiguity and semantic overlap, which constrain the effective- ness of editing. To address these issues, this paper proposes Conflict-Awar Knowledge Editing

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framework for unstructured text in the wild (CAKE). The framework comprises two key compo-
nents: (1) semantic-augmente Graph Representation module (SGR) (Section 2.2 and (2)
Conflict-aware Knowledge Editing (CKE) strategy (Section 2.3
2.1 Preliminary: Wild Unstructured Knowledge Editing
Traditional knowledge editing methods rely on structured knowledge instances K
[ki}
{(s; rú, o,)}. typically represented as triples, to perform model updates. S; and o, denote the subject and object entities and r; the relation. During the editing training, the explicit input query is composed of the subject entity and the relation, i.e., s; 3 supervisory signals as the prediction target.
while the object entity provides
In contrast, Wild Unstructured Knowledge Editing (WUKE) task [2 necessitates updating language models using solely unstructured textual data devoid of explicit query-target training pairs while evaluating model fidelity through wild complex queries that reflect real-world linguistic diversity. A straightforward solution involves extracting discrete knowledge triples from unstructured text and naively replicating the structured editing paradigm by constructing artificial query-target pairs.
However, this adaptation fundamentally misaligns with the nature of unstructured knowledge: whereas structured editing operates on isolated triples k, during the whole training phase, WUKE requires single-edit injection of multiple interconnected triples extracted from cohesive textual contexts in only one edit. To clarify the symbolic distinction, we define k,j (e,, Tay, e2) for WUKE's interconnected triples contrasting with structured editing's knowledge atomic K (k. } {(s,, Fi, o:)). e; and ej denote the subject and object entities and rij is the relation. Corresponding pseudo-queries Xiy e; ray could also be analogously constructed, yet ensuring query-answer training.
We adopt the classical parameter-preserving methods as [15], which preserve the original model performance more effectively by introducing additional parameters. Given a pretrained model M with layer weights W, an unstructured text T, and the extracted knowledge triples K = {kiz parameter-preser preeeeerrrpeeeeiiggggg. methods augment W with extra parameters A W. The forward pass of the editing layer of the input query X;j can be denoted as h, = Wa + A Wa,j, where a, is the representation of x before feed into layer W. The editing process can be expressed as:
AW - AW - 17
VLCE (oj, (e.x))))
K
where 1) is the learning rate, M,- denotes the model after editing step - 1 with the additional parameters, Loe is the cross-entropy loss.
In this paper, we propose the Conflict-Aware
Knowledge Editing (CAKE) framework, which com- prises two key components: a Semantic-augmented Graph Representation module to mitigate repre- sentation ambiguity, and a Conflict-aware Knowledge Editing strategy to resolve the editing conflicts caused by semantic overlap.
2.2 Semantic-augmenter Graph Representation
The inherent semantic entanglement and complex interdependencies in unstructured text lead to ambiguous knowledge representations under conventional structure-base based methods. To address this, we propose the comantic-augmented Graph Representation (SGR) module, which holistically enhances knowledge encoding through synergistic structural, relational, and diversity-aware semantic reinforcement, optimizing representational granularity and preserving contextual integrity.
Graph-based Structural Disambiguation To holistically capture the latent topological configu- rations of textual knowledge, we perform dynamic graph induction to establish context-preservee knowledge structures. Unlike discrete triple extractions that fragment relational semantics, our graph-constrained representation paradigm systematically preserves inter-knowledge associations at paragraph-level granularity, maintaining ecological knowledge coherence through structural-semanticc alignment. Specifically, we first design a structure-aware prompt template that steers the foundation model M to dynamically construct knowledge graph from input text T, ensuring isomorphic mapping between the unstructured text and their underlying knowledge topology:
G = (E, M([TTT)
(2)

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where E = (e1, C2, e denotes the entity set. R = {riz} represents the direct relation set. Here Fiş denotes the direct relation between entity e, and e; lexically instantiated in T, yielding a sparse graph structure. The prompt template I' begins with the instruction: "Extract interconnected knowledge graph from the paragraph, emphasizing semantic relationships among the extracted triples." " Further details are provided in the Appendix.
Multi-hop Relational Enrichment While G encodes the explicit relations among entities in unstructured text T, its latent relational topology remains discontinuous. Establishing multi-hop relational inference pathways between entity pairs enhances comprehension intricate semantics, enabling robust knowledge editing.
To enhance the semantic continuity, we further guide language model . to infer latent dependencies between distantly connected entities through p-hop dependencies:
Fiy = -M(T" G,T, (es, e;)l), where de (ei, E [2. R 1++R' (r,gr)
(4)
Where 1st denotes the multi-hop reasoning instruction (begin with "Given a knowledge graph extracted from a text and two nodes, infer and describe the relationship connecting these two nodes.") ")dees""),, and de indicates the path length between entities in G. The augmented relations R allows cross-hop semantic bridges, effectively densifying the knowledge graph's 's relational manifold while maintaining textual grounding constraints.
Paraphrase-driven Semantic Diversification To address generalization challenges in unstructured editing scenarios, we implement straphrase-driven Semantic Diversification, enhancing knowledge representation diversity through controlled lexical variation. For each relational fact (e,, Fij: e,) from constructed knowledge(ri E R*), we guide the model M through lexical diversification instruction Is to generate paraphrastic variants Rd to augment the semantic diversity:
(rly) = M([ G, (e,, Fij, ej)l), where rij R R - R
(5)
(6)
The instruction template 1s begins with "Rewrite each relation between connected nodes with a semantically equivalent alternative. ") Crucially, the paraphrase-driven diversification operation establishes a bijective mapping between each original relation rij E R' and its generated variant set } effectively transforming the initial simple graph G into directed multigraph G'
(E, R U ) with multiple parallel edges between entity pairs. This iterative augmentation of edge multiplicity enhances the graph's capacity to encode divergent realizations while preserving semantic invariants, thereby improving robustness against distributional shifts in real-world queries.
The tripartite semantic enhancement framework systematically captures multifaceted relational semantics between entities through structural disambiguation, relation enrichment, and semantic diversification. This synergistic integration dynamically densifies the knowledge representation space derived from unstructured text, establishing comprehensive knowledge manifolds that holistically encode both explicit assertions and latent dependenciesffaclliatinn the precise knowledge editing.
2.3
Conflict-aware Knowledge Editing
Distinct knowledge units extracted from unstructured text exhibit dense semantic overlaps, which induces conflicting editing trajectories. Unlike conventional structured knowledge editing paradigms that process weakly correlated knowledge instances, unstructured text editing confronts semantic overlap phenomena- phenomena-innsaanee -nntanncssssssseeesssseeee where lexically congruent inputs demand divergent knowledge updates. During gradient- based optimization, these semantically equivalent but knowledge-diverge samples induce conflicting gradient directions.
Therefore, we propose Conflict-aware Knowledge Editing (CKE) strategy, which formulates the conflicted editing as graph-coloring problem. CKE first derive knowledge conflict graph based on our cemanti--augmented knowledge graph, and resolves conflicts by assigning distinct colors to conflicting knowledge triples, with each color mapping an isolated parameter editing subspace.
5

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Specifically, the conflict graph G = (K, ②) is constructed, where each vertex ky represents a knowledge instance Eaj. ez). The edge function Φ K x {0, 1] defined as:
{
if a I[ op) + B. || |appl 27
(kky kpg)
0 otherwise
where aij denotes the hidden representation of Xij = e, @ Fij at the editing layer and T(-) is the Kronecker delta indicator function. Hyperparameters a, 8 [0, control the weights of the subject entity sharing and the query(entity and relation) similarity, respectively, and y serves as the conflict determination threshold.
is noteworthy that our conflict detection specifically targets knowledge triples with divergent object entities while maintaining shared subject entities semantically similar relations. For parallel edges in G' (i.e., triples (c,, Qÿ) and (c,, r'j. C2) with identical subject-object pairs), we explicitly designate them conflict-free instances and allocate them to the same editing expert. This design rationale explains why Equation 7 focuses solely on the relation component r.j and subject entity, as subject-relation pairs constitute the primary confiit determinant while object entities serve as resolution targets.
Following the construction of the knowledge conflict graph, employ the Welsh- algorithm to compute the vertex coloring scheme ff and determine the minimum chromatic number L, where f(ky c₁ assigns the knowledge instance kij to the I-th color. We initialize set of LoRA [T5] parameter subspaces AW1, AW..... W. with each subspace updated via gradient descent:
AW K AW, 1)
Σ LM (xig)))) f(kiy)=
where 17 denotes the learning rate. During post-edit inference, due to the parameter independence among distinct LoRA experts, we aggregate their forward outputs via averaging. To effectively distinguish edited knowledge from the model's original knowledge during inference, we maintain dynamically preserved knowledge activation vector serving as memory routing mechanism. Specifically, this memory router computes the similarity between the input activation and stored activation memories; if the similarity falls within a defined threshold, the input considered within the edit scope. In such cases, outputs are generated by integrating all LoRA subspace parameters along with the original model parameters; otherwise, output relies solely on the original model parameters.
Through our SGR module and CKE strategy, we effectively enhance ambiguous semantics unstruc- tured texts while mitigating editing conflicts arising from semantic overlaps across heterogeneous knowledge components. This framework significantly improves knowledge editing performance in wild, unstructured textual environments.
Experiments and Results
3.1 Experimental Settings
Following the standard experimental settings of the WUKE task [12 we evaluate the effectiveness of our method on unstructured text editing using three datasets: Coun- terFact [8) WikiUpdate [12], and MQuAKE-CF [16]. Consistent with [1], the unstructured text in these datasets is derived from the expansion of specific structured QA pairs. Among them, the CounterFact dataset contains large amount of counterfactual knowledge, exhibiting a high degree of knowledge conflict within the text. The WikiUpdate dataset features individual texts that encompass richer and more diverse range of knowledge entities, while the MQuAKE-CF dataset includes more multi-hop relationships among different knowledge points within each text. We assess the editing performance by measuring the accuracy of the model's responses to specific questions after editing. Two key metrics are employed: Reliability (Rel.), which measures the accuracy of the model's s answers to questions related to the edited unstructured text, and Locality (Loc.), which evaluates the model's accuracy on questions unrelated to the edited content after the knowledge editing process. For the locality evaluation, use QA pairs from the ZsRE dataset [17] that semantically unrelated to the edited samples. Since the WUKE task evaluates editing reliability through abstract questions derived from unstructured text, this reliability metric in fact simultaneously assesses both reliability and generality as defined in conventional structured knowledge editing.
Datasets and Evaluation.

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Table 1: Editing Reliability and Locality. Other comparison methods follow the test method set in AKEW. The test samples of Locality are selected from the samples the ZsRE dataset that are semantically irrelevant to the edited samples for testing.
Model and Editing Method
Datasets
CounterFact
MQuAKE-CF
WikiUpdate
Model
Editing Method
Rel((%)
Loc, %
Avg( %)
Rel. %))
Loc.(%)
Avg %
Rel. %)
Loc.(%
Avg %
ROME
7.84
61.21
34.52
34.84
59.86
47.35
30.13
61.11
45.62
MELO
1.37
64.10
32.74
19.14
63.15
41.15
29.15
63.24
46.20
MEMIT
5.69
59.04
32.37
33.90
60.28
47.09
34.56
62.29
48.43
GPT2-XL
WISE
10.01
24.16
17.09
28.58
23.10
25.84
31.27
25.33
28.30
Elder
23.10
60.67
41.89
38.28
57.33
47.81
25.31
62.20
43.76
Ours
35,04
61.40
48.22
62.01
61.49
61.75
42.26
62,38
51.80
ROME
11.56
81.28
46.42
39.56
82.74
61.15
42.43
80.68
61.56
MELO
4.61
73.30
38.96
32.73
80,27
56.50
45.87
70.40
58.14
Llamma32-3B
MEMIT
15.41
80.16
47.79
42.15
81.65
61.90
47,80
75.14
61.47
WISE
4.46
14.84
9.65
19.61
11.31
15.46
4.09
17.65
10.87
Elder
26.04
79.23
52.64
49.19
80.44
64.82
35.40
72.30
Ours
43.24
83.67
63.46
64.23
80.53
73.12
53.10
71.42
62.26
ROME
11.64
87.32
49,
43.54
65.49
53.11
MELO
3.43
87.14
45.29
34,04
86.31
60.18
57.71
86.45
72.08
Llamaa-8b
MEMIT
22.76
87.69
55.23
50.86
87.71
69.29
56.54
88,38
72.46
WISE
21.51
44.84
33.18
47.40
43,10
45.25
28.98
51.41
40.20
Elder
1.99
87.27
44.63
56.13
81.50
68 82
58.23
84.35
71.29
Ours
38.19
86.61
62.40
57.74
86.42
72.08
51.44
87.57
69.51
Baselines. We select representative knowledge editing approaches for comparison, including ROME [8) MEMIT [9), WISE (7), Melo [88] and Elder [19]. Since these methods cannot directly perform edits using unstructured text, we follow the settings of AKEW [12) by extracting corre- sponding triple from each sentence in the unstructured text and employing these triples as inputs for the editing process in the respective methods.
Implementation Details. We validate the effectiveness of the proposed method on three widely
adopted large language models (LLMs): GPT2-XL [20], LLaMA3.2-3B (21, and LLaMA3-8B 21] The default hyperparameter settings are == 0.8, p 0.0,8 and y = 0.8 (see Formula in Section (.3to All experiments conducted on NVIDIA GPUs, and the editing optimizer is SGD. More experimental details are outlined in the Appendix
3.2 Main Results
Table presents the comparative editing performance of our approach and several mainstream knowledge editing methods. Our method CAKE demonstrates superior editing reliability on all three unstructured datasets and achieves comparable results with existing methods in terms of editing locality. The stable performance across different datasets highlights the generalizability our approach to various types of unstructured texts.
Performance on the CounterFact dataset. CAKE achieves outstanding results on the CounterFact dataset, outperforming all existing methods across the three I.LMs. Specifically, it surpasses the second-best approach by 11.94% 17.20%, and 15.43% reliability. This advantage arises because the CounterFact dataset features significant conflicts between knowledge items and substantial semantic overlap. To address this, our method introduces a graph-coloring-hann based conflict resolution strategy, which effectively mitigates parameter update inconsistencies caused by knowledge conflicts during editing. Additionally, the incorporated Paraphrase-driven Semantic Diversification mechanism enriches the semantic representations of identical knowledge, substantially improving the editing outcomes for CounterFact samples where test queries differ considerably from the textual triples.
Performance on the MQuAKE-CF dataset. On the MQuAKE- dataset, CAKE surpasses the reliability of the second-best baseline by more than 15% across all three LLMs. This remarkable improvement can be attributed to the semantic enhancement and conflict resolution mechanisms embedded in CAKE. The MQuAKE-CF dataset presents higher level of conflict and editing difficulty due to its inclusion of more intricate reasoning dependencies among knowledge entities, such multi-hop relationships. Our method effectively exploits these implicit relationships through
7

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Table 2: Ablation Study Results. (Reliability % each Datasets) Avg. represents the mean reliability of the three datasets in the corresponding editing methods and models.
Model
Ablation Method
Avg.
CounterFact
MQuAKE-CF
WikiUpdate
without SGR
45.94
34.63
62.19
41.22
without CKE
44.13
30.06
61.51
40.84
GPT2-XL
Router Expert Ours
28.89
23.10
38.28
25.31
46.10
35.04
62.01
42.26
without SGR
48.63
38.38
58.94
48.56
without CKE
50.46
34.70
65.71
50.77
Llama3.2-3B
Router Expert Ours
36.87
26.04
49.19
35.40
53.52
43.24
64.23
53.10
without SGR
47.62
37.28
55.53
50.06
without CKE
47.59
25.66
56.13
60.99
Llama3-8b
Router Expert
30.98
1.99
32.72
58.23
Ours
49.12
38.19
57.74
51.44
hoowledg-ggrah-baaee multi-hop reasoning, enabling the model to comprehensively capture and utilize the complex semantic relations inherent the original text.
Performance on the WikiUpdate dataset. On the WikiUpdate dataset, CAKE demonstrates supe- rior editing reliability, exceeding the second-best approach by 7.7% GPT2-XL. This improvement arises from the dataset's s rich inclusion of diverse knowledge entities, where conventional triple extraction techniques often capture only sentence-level associations and fail to account for semantic ambiguitics inherent in unstructured text. Our method effectively addresses this limitation through Multi-hop Relational Enrichment, which enhances the detection of inter-entity associations across the entire text and identifies additional knowledge pairs, thereby improving editing reliability. In terms of locality across all models, approach remains comparable to other state-of-the-ar methods.
3.3 Ablation Study
We conducted module ablation experiments all settings in the main comparative study, compar- ing the editing reliability before and after module removal to evaluate the effectiveness the two key components in our method: SGR and CKE. For the ablation of the SGR module, we removed the knowledge graph construction and semantic enhancement processes from the original method. Instead, adopted the knowledge extraction approach from AKEW convert unstructured text into a set of triples, while retaining the CKE module for knowledge editing. As shown Table 2 the editing reliability of the three LLMs decreased by 0.16%, 4.89%. and 1.5%, respectively, compared with the full model. This demonstrates the crucial role of SGR in enabling effective knowledge extraction and semantic enhancement from unstructured text.
For the ablation of the CKE module, two experiments were designed. First, the without CKE setting, all knowledge representations enhanced by the SGR module were fed into single LoRA expert for editing. Across the three LLMs, this configuration resulted in a reduction in average editing reliability by 1.97%, 3.06% and 1.53%, respectively, compared the complete method. Second, to evaluate the expert allocation effectiveness of CKÉ, we replaced the graph-coloring- allocation mechanism with gated routing boased LoRA expert allocation approach, following the design of Elder. This substitution caused an average decrease of over 15% in editing reliability across all three datasets and models. Collectively, these ablation results validate the effectiveness of both the anmmntte-augmented representation within SGR and the graph-coloring-bann based expert allocation mechanisms within CKE, highlighting their importance in conflict resolution and in enhancing editing reliability under unstructured text scenarios.
expert
3.4 More Analysis
Analysis of Conflict-aware Hyperparameters. In order to explore the impact of the hyperparame- ters controlling the weights of subject entity sharing and query (entity and relationship) similarity in the formula in Section 2.3 for conflict detection of knowledge instances in the CKE module on

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Impact of Conflict Ratio on Reliability
Impact of Number of Nodes on Reliabiliity
(confict_no <50%)
single-expert
1-4)
OuTS ours
sing e eeppert
(confict ratio 50% 75%) teonfict 775%)
DUTS
single expert
(num roces 5-7)
DUPS
single-expert
Inum_rodes 7-15)
Reliabiltty
Rellabilit
GPT2-Xl
llama3.2-30 2-bbb
1lama3-8b
GPT2-xl
llama3.2-3b
llama3-Bb
Figure 4: Analysis of the number of nodes in knowledge graph and confiit ratio between knowledge on the reliability of knowledge editing. The average Reliability test results the three datasets CounterFact/Wikiie Wiiter//eWWlllpleeeMAKAAKKOCG
editing reliability, we observe the impact of different parameter weights on the results by adjusting the values of a/" and B/y in the formula. The experimental results shown in the figure below:
As can be seen from the Fig. the settings of subject entity sharing and the query (entity and relation) similarity in the conflict detection function of the CKE module are reasonable. Both can reflect the conflict of different knowledge the editing process and will have an impact on editing reliability, higher similarity detection weight will cause two more similar knowledge examples to be assigned to different experts, thereby improving editing reliability. And the corresponding weight in the conflict detection function increases, the separation effect of conflicting knowledge will be better.
Analysis of Knowledge Conflict and Editing Reliability. To further investigate the relationship among the volume of knowledge, the degree of knowledge conflict, and edit- ing reliability within the knowledge graph, we quantified the degree of knowledge conflict in the CKE algorithm. Specifically, it is defined as the ratio between the num- ber of allocated LoRA experts and the total number of knowledge items. As illustrated in Figure the experi- mental results demonstrate that as the amount of knowl- edge increases, the editing reliability generally declines. This observation suggests that the scale of knowledge contained in unstructured text impacts the overall editing performance. However, compared with the single-expert LoRA editing approach, method exhibits noticcably slower degradation trend, achieving consistently higher reliability across all knowledge scales.
6.75
8.430
0,030
0.400
5,425
0.150
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0.410
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Figure 3: Analysis Conflict-aware Hy- perparameters LLaMA-3.2.
Moreover, the results indicate that as the degree of conflict among knowledge items within the knowledge graph increases, the editing reliability of the single-expert LoRA method decreases markedly. In contrast, our method maintains a relatively stable level of editing performance under varying degrees of knowledge conflict. These findings highlight that the CKE algorithm- based on knowledge graph
mitigates the performance degradation typically caused by increasing knowledge volume and conflict. This robustness arises from the CKE mechanism's s capacity to assign potentially conflicting knowledge (i.c., knowledge that may induce opposite parameter update directions) to separate LoRA experts, thereby isolating their respective update processes and ensuring stable and reliable editing outcomes.
Related Work
Knowledge Editing, Current knowledge editing methods typically rely on carefully curated struc- tured facts as input and evaluate model retention by querying the fact prompts used during the editing introduces additional parameters into the model through low-rank adaptation matrices. Melo 18] constructs a knowledge vector database that incorporates counterfactual rea- soning, while ELDER [19 integrates multiple LoRAs through router network trained to establish
process. LoRA [15

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smooth associations among data adapters, However, structured facts are often derived from manually designed datasets and fail reflect the true source of knowledge pydates-namely unstructured text. In the setting proposed by AKEW [12. the model edits knowledge using triples extracted from each sentence of unstructured text and is evaluated with queries that correspond to these unstructured facts. Under this setting, editing accuracy drops significantly. Recent studies, such as UnKE [2) and AnyEdit [I]], have proposed methods for editing unstructured answers. However, these approaches primarily focus on scenarios in which complete long text serves as the answer to specific question, incorporating preset queries into the editing process. This setup differs fundamentally from that of AKEW, which performs knowledge editing directly wild, unstructured text without predefined question constraints. To the best of our knowledge, CAKE is the first method specifically designed for the Wild Unstructured Knowledge Editing (WUKE) task.
Knowledge Extraction and Modeling. Knowledge Extraction and Modeling methods can be broadly categorized into two types: traditional methods and those leveraging Large Language Models. Traditional methods for unstructured text knowledge extraction include pipeline and joint models. from rule-based Open 23 to machine learning (e.g., SVM (4
Pipeline approaches--anginn
and deep learning models like BiL.SMM-RRF 25), CNN/RNN 26 -sfffer from error propagation and limited entity interaction. Joint models such as Copy (27), TPlinker[28), and MBGAB (29) mitigate cascade errors but struggle to capture implicit semantics and global context due to their reliance surface patterns.In In contrast, large language models enable flexible and context-aware knowledge extraction and representation by leveraging pre-trained world knowledge and generative capabilities. They can integrate scattered information, infer implicit relations, and construct structured knowledge with stronger generalization beyond explicit textual cues 30 31 32).
Conclusion
In this work, we first identify the challenging representation ambiguity and editing conflicts lying in Wild Unstructured Knowledge Editing task, and specifically design a Conflict-Aware Knowledge Editing (CAKE) framework for wild unstructured text. The framework integrates a Semantic- augmented Graph Representation module for precise and enriched knowledge encoding, alongside Conflict- Knowledge Editing strategy that isolates conflicting edits through graph- parameter subspace allocation. Extensive evaluations on the WUKE benchmark verify that CAKE significantly enhances editing accuracy and stability compared to prior approaches. Our method opens a promising path toward effective and confiict-reilientt knowledge editing directly on unstructured text, fostering broader adoption of model editing in real-world scenarios.
Acknowledgment. This work was supported by the Fundamental Research Funds for the Central Universities.
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Appendix
A. Limitations and Broader Impacts
The limitation of our method is that it can only be applied to LLMs designed for next-token prediction, which makes unable to adapt to the knowledge editing LLMs of all architectures. For knowledge editing tasks with strong professionalism in specific field, during the graph construction process of SGR, it necessary to use the LLM corresponding to the specific field to guide the graph construction and ensure the accuracy and professionalism of the knowledge graph construction.
addition, due lack of consideration of the authenticity of unstructured text some malicious people may use it to incorrectly edit the model to spread false views such as discrimination and prejudice. We suggest that in the future, more supervision should be introduced on the correctness of all texts and other knowledge sources before editing to ensure the reliability of the edited models terms of ethics and social responsibility.
framework,
B. Implementation Details
B.1 Editing Details
In the SGR module, for language models capable of accurately following graph-consiruction instruc- tions (e.g., Ll)ma3-8B), the editing process directly performed by the model itself. For models with limited -ostucttionfollowinng and generation capabilities (e.g.. GPT2-XL),, auxiliary assistance provided by more capable large language models, such as GPT-4. All training and evaluation procedures conducted NVIDIA A800 80GB GPUs. Regarding the editing layer of the model, for GPT2-XL, we select "transformer: h. 36.ml. 16.mlp.c. c_fc"" and "transformer: h. m1p. c_fc"" for editing. For 1lama3.2-3B and Ilama3-8B, select "model.layers 1ayers. 13. mlp. down__rr "oon"_ for editing. During the editing training phase across all experiments, the learning rate set to 0.001, and the maximum number iterations fixed 50. For experiments involving LoRA Abbase methods, the rank of each individual LoRA adapter is set to 8.
B.2 Detailed Instructions in SGR
To support the Semantic-augmented Graph Representation (SGR) module, we define three instruction formats to guide large language models in constructing and enriching unstructured knowledge graphs. Each instruction is accompanied by an in-context example to clarify its intent and output format, followed by an additional example to illustrate generalization.
1. Structure-aware Graph Construction
Instruction of Structure-aware Graph Construction with Example
Task:
Extract an interconnected knowledge graph from the following paragraph. Each triple should represent clear semantic relation between entities explicitly stated in the paragraph. Preserve the contextual semantics. Output as a list of triples in the form: (Entity 1, Relation, Entity2).
Example Input/Output:
Input Paragraph:
"Alan Turing developed the concept of a Turing machine, which became the foundation of computer science. He worked Bletchley Park during World War II."
Output Triples:
(Alan Turing, developed, concept of Turing machine)
(Turing machine, became the foundation of, computer science)
(Alan Turing, worked at, Bletchley Park)
(Bletchley Park, active during, World War II)
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2. Multi-hop Relational Enrichment
Instruction of Multi-hop Relational Enrichment with Example
Task: Given knowledge graph extracted from paragraph and two nodes, infer and describe the semantic relation that connects them indirectly via multi-hop reasoning. Ensure the relation reflects coherent real-world semanties grounded in the original text.
Example Input/Output:
Input Graph:
(Marie Curie, discovered, radium)
(radium, used in, cancer treatment)
Text:
"Marie Curie discovered the radioactive element radium, which later found applications in cancer therapy.
Query: (Marie Curie, 2, cancer treatment)
Output: (Marie Curie, discovered substance used in, cancer treatment)
3. Paraphrase-driven Semantic Diversification
Instruction of Paraphrase-driven Semantie Diversification with Example
Task: Given knowledge graph with triples, rewrite each relation in semantically equivalent but lexically different way to diversify the expression of the knowledge. Keep entity names unchanged. Return paraphrased triples.
Example Input/Output:
Input Triple: (Nikola Tesla, invented, alternating current)
Output Paraphrases:
(Nikola Tesla, was the inventor of, alternating current)
(Nikola Tesla, came up with, alternating current)
(Nikola Tesla, pioneered, alternating current)
B.3 Details of the activation-similartty routing mechanism
In our framework, the activation-similarity routing mechanism is not central design component, as the underlying principle is relatively conventional. Consequently, it is not emphasized in the main body of the paper. The activation activation-similarity routing employs a hard activation- bbased routing strategy to determine whether an input instance falls within the scope previously edited knowledge. Specif- ically, during the editing phase, preserve low-bit lightweight activation vectors corresponding each edited knowledge item. During inference, the cosine similarity between the input prompt and the stored activation representations computed. If the similarity exceeds a predefined threshold (set to 0.7 in our experiments), the input is deemed relevant to the edited knowledge, and the corresponding LoRA module activated for inference. Otherwise, the model relies solely its original parameters without invoking any edited components.
C. Descriptions of Compared Model Editors
We compare method with five representative model editing approaches. Below summarize their core mechanisms and editing properties.
ROME g ROME identifies the internal location of factual associations L.MSs using causal tracing, assuming that MLP layers are the main knowledge carriers. It edits the value matrix of
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single MLP layer using closed-form rank-one update derived from least squares regression. ROME injects one fact per iteration, enforcing precision through a Lagrangian remainder, but lacks scalability in multi-edit or lifelong scenarios.
MELO assumes that attention heads store factual knowledge and adopts discrete adapter allocation strategy. It assigns a dedicated LoRA adapter to each knowledge edit, enabling isolation and preventing interference. Although effective in reliability, its discrete mapping is brittle to paraphrased or slightly varied imputs, making less robust for generalization lifelong settings.
MELO [8]
MEMIT builds on ROME'S assumptions, treating FFNs knowledge key-value stores, and extends editing to multiple layers and multiple facts simultaneously. It directly modifies FFN parameters using batched least-squares updates, supporting thousands of edits. However, edits accumulate in the same parameter space, which can cause interference and poor locality during lifelong updates.
MEMIT [
WISE 7
WISE introduces dual parametric memory mechanism bridge the gap between long-term memory (model parameters) and working memory (retrieval-based representations). edits only the side memory-a copied FFN value and routes queries between main and side memory based on an activation-based routing mechanism. To support lifelong editing, WISE uses knowledge sharding into orthogonal subspaces and merges them via Ties-Merge, achieving high reliability, locality, and generalization even with thousands of edits.
ELDER enhances lifelong model editing by adopting a Mixture-of-LoRA (MoL) approach, replacing discrete adapter selection with continuous, learnable router that adaptively combines top-k LoRA modules per input. This enables robust handling of semantically equivalent paraphrases. A guided loss aligns edit knowledge with LoRA allocations, while a deferral mech- anism detects whether an input needs editing, allowing fallback to the original model for general tasks. ELDER avoids the scalability bottleneck of discrete adapter assignment and maintains strong generalization and reliability under long edit sequences.
ELDER 19]:
D. More Experiments
D.I Editing Efficiency
To evaluate the training efficiency of the proposed editing framework, we compared its per-sample editing time with that of several representative baseline methods. As shown in Table 3 the proposed method demonstrates superior efficiency during the editing process. Compared with post-hoc editing approaches such as ROME and MEMIT, the isolated low-rank parameter training performed by the LoRA mechanism achieves convergence with substantially lower computational cost. Moreover, in comparison with methods such as MELO and Elder, our approach benefits from the coloring strategy based on the Welsh Powell algorithm in the CKE module, which climinates the need for additional training. Consequently, it is more efficient than both the expert-selection gating mechanism of Elder, which requires extra training. and the MELO method, which entails extensive vector data access. These results collectively indicate that the proposed method achieves notable advantages in editing efficiency compared with existing model editing approaches.
Table 3: Comparison of average editing time per sample (in Tested dataset MQuAKE-CK
Tested on Llama3-8b from the
Method
ROME
MELO
MEMIT
WISE
ELDER
Ours
Time (s) 78.64
52.76
77.23
143.91
54.17
49.31
D.2 Analysis of threshold in activation-similarity routing
The Figure 5 below illustrates the effect of the similarity threshold in activation-similarte routing on the CounterFact dataset using llama3.2. As the threshold decreases, editing reliability tends to decline, while locality improves. This trend arises because lower threshold enforees stricter
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Effect of Similarity Threshold on Editing Performance
100
localit
80
(%)
60
40
20
0.4
Similarity Threshold
Figure 5: Performance different similarity thresholds activation-similarity routing.
criterion for LoRA expert activation, leading the model to depend more heavily on its unedited parameters during inference.
D.3 Evaluation of the Initial Knowledge Graph Quality in SGR
The quality of the initial knowledge graph is fundamental to CAKE's success. Through manual evaluation, we verified that CAKE's initial knowledge graph accurately and comprehensively covers nearly all knowledge entities present in the original unstructured passage. We report the corresponding evaluation results in the Table We conducted human evaluation from two perspectives: extraction accuracy and knowledge completeness. As be seen, CAKE's IKG achieves near hear-perfect coverage of the entities and relations present in the raw text. This strong performance mainly stems from (i) CAKE's carefully designed, task-specific prompting rules and (ii) the capabilities of the employed LLM (for models with limited intrinsic extraction capacity, such as GPT2-XL, we leverage the GPT-4 API for IKG construction).
Table Evaluation of the Initial Knowledge Graph Quality in SGR.
Datasets
knowledge completeness
extraction accuracy
CounterFact MQuAKE-CF
0.99
0.95
0,99
0.97
WikiUpdate
0.93
0.95
D.4 Detialed Ablation of SGG
Table 5: Detialed Ablation of SGR. Each component within the SGR module yields a distinct improvement editing performance.
Method w/o SGR
w/o Multi-hop Enrichment
w/o Paraphrasing
CAKE
Time (s)
38.38
39.78
41.66
43.24
To further verify that each semantic enhancement component within the SGR module contributes meaningfully to the overall editing performance, we conducted ablation studies on the Multi-hop Relational Enrichment and Paraphrase-driven Semantic Diversification stages. These experiments were designed to examine how representational ambiguity influences the model's editing capability. All evaluations were performed on the CounterFact dataset using the Llama3.2-4 3.2BBBBBBBBBB model, and the results are summarized in Table As shown, each component within the SGR module yields distinct improvement in editing performance, confirming that the proposed Multi-hop Relational Enrichment and Paraphrase-driven Semantic Diversification mechanisms effectively enhance the model's 's ability to capture diverse relational structures and achieve more comprehensive representation factual knowledge contained in unstructured text.
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E. End-to-End Case Study of CAKE Framework
Below I will construct complete unstructured text knowledge editing process example based on the method framework (CAKE, including SGR and CKE), covering the entire process from:
Step 0: Input Unstructured Text
Step 1: Structure-aware Graph Construction
Step 2: Multi-hop Relational Enrichment
Step 3: Paraphrase-driver Semantic Diversification
Step 4: Conflict Graph Construction
Step 5: Conflict-aware Graph Coloring
Step 6: Final Answer after Editing
Step 0: Input Unstructured Text
Input Paragraph:
"Marie Curie discovered the radioactive element radium, which was later used in cancer therapy. She received Nobel Prize for her contributions. Radium decays into radon, which a health hazard. Her husband Pierre Curie was also physicist."
Step Structure-aware Graph Construction
Using structure-aware prompt to extract knowledge triples:
Output Triples:
(Marie Curie, discovered, radium)
(radium, used in, cancer therapy)
(Marie Curie, received, Nobel Prize)
(radium, decays into, radon)
(radon, is, health hazard)
(Pierre Curie, was, physicist)
(Pierre Curie, was hushand of, Marie Curie)
Resulting in base knowledge graph G (E, R*).
Step 2: Multi-hop Relational Enrichment
Using prsdpt-based inference for disconnected entity pairs:
Example: For (Marie Curie, cancer therapy)
Output: (Marie Curie, discovered radium used in, cancer therapy) This relation is added to form the enriched graph G.
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Step 3: Paraphrase-driven Semantic Diversification
For each (e,, Tij; eÿ) in R generate lexical variants:
Input: (Marie Curie, discovered, radium) Paraphrases:
(Marie Curie, the discoverer of, radium)
(Marie Curie, identified, radium)
(Marie Curie, found, radium)
The resulting graph becomes a multigraph G' = (E, R J
Step 4: Conflict Graph Construction
Construct the conflict graph G = (K, ④) where each node a knowledge triple kij (et, Fij, ej).
Example Conflict Detection of k12 with triples kiss ka1t
h12 (Marie Curie, received, Nobel Prize)
kis! (Marie Curie, discovered, radium)
k1 (Pierre Curie, was husband of, Maric Curie)
Conflict Detection Criterion:
okky, kpg)
(0(12,, k1s) = 1
(high subject relation semantic similarity)
(0(12, k41) = O
(low subject or relation semantic similarity)
Hence, in the conflict graph G k12 has an edge with K13, but not with K11-
Step 5: Conflict-aware Graph Coloring
Apply the Welsh-Powell algorithm on G assign edit-isolating colors. For three knowledge triples k12, k13, kn
Vertex Coloring Result:
Color (assigned AW1): k12 (Marie Curie, received, Nobel Prize)
Color 2 (assigned to AW2): æia (Marie Curie, discovered, radium)
Color (shared with k12): han (Pierre Curie, was husband of, Marie Curie)
Note that hi2 and K4ı share no conflict and thus share LoRA subspace AW,
Update Rule per Color Group:
AW, + AW - Σ ((M(x)))
(9)
f(kk))=i
During optimization:
Color (AW1) updates k12 and kn
Color 2 (AW2) independently updates Ki to avoid gradient conflict with k
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Step 6: Final Answer after Editing
At inference time, all LoRAs to perform inference.
Query: "Did Marie Curie receive the Nobel Prize?" Edited Model Output: "Yes, she did."
26


Reviews:
[{"review_id": "o6JSpRHLHr", "reviewer": "Reviewer_7oCG", "summary": "The paper addresses the challenge of “wild, unstructured knowledge editing” (WUKE) for large language models—updating facts when supervision comes only from raw text—by proposing CAKE, a two-stage framework that first derives a Semantic-augmented Graph Representation (SGR) of each paragraph through LLM-prompted extraction, multi-hop inference, and paraphrase diversification, then performs Conflict-aware Knowledge Editing (CAKE) by treating each triple as a node in a conflict graph, colouring that graph with the Welsh–Powell algorithm, and mapping each colour to an orthogonal LoRA sub-space so conflicting edits do not interfere during fine-tuning. Tested on CounterFact, WikiUpdate, and MQuAKE-CF with GPT-2-XL and Llama-3 (3 B & 8 B), CAKE improves reliability by 11–21 percentage points over strong baselines while preserving locality, establishing the first conflict-aware framework for unstructured text edits, introducing a richer graph encoding of paragraph knowledge, and offering an elegant, parameter-efficient gradient-isolation mechanism via multi-expert LoRA.", "questions": "1. Equation 7 uses fixed thresholds (α, β, γ) and a single hidden layer to compute conflict edges; how robust are results to these choices?\n2. The paper briefly mentions “activation-similarity routing”; what exact algorithm, similarity metric, and thresholds are used, and how does averaging vs. hard selection affect results?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Clear motivation\nEditing directly from unstructured text is both realistic and under-explored; the paper articulates why traditional triple-based editors fail and illustrates the two core difficulties with concrete examples (Fig. 1).\n2. Methodological originality\nSGR cleverly blends LLM-prompted extraction, reasoning, and paraphrasing to densify the knowledge graph, a departure from pipeline OpenIE. CKE uses a classical colouring algorithm to allocate orthogonal LoRA experts—an elegant, lightweight way to resolve conflicting updates.\n3.  Solid empirical gains\nReliability improvements (up to +20.9 pp on Llama-3 3 B) are large, consistent across datasets, and ablations isolate the contribution of each module (Table 2).\n\nWeaknesses\n1. Scalability concerns\nWelsh-Powell is O(|E| log |E|); constructing and colouring a graph for thousands of triples per edit may bottleneck. Suggestion: discuss complexity, memory footprint, and potential batching or heuristic colourings for very large documents.\n2. Clarity & writing\nSome sections (e.g., 2.2 “directed multigraph G′”) use dense prose and could benefit from concrete pseudo-code. Typographical errors (“editing confilicts” l. 86) and inconsistent notation (rij vs. r′ij) should be fixed.", "was_revised": true, "final_justification": "The authors addressed most of my concerns. I raised the score.", "rebuttal": "## Response to Reviewer 7oCG\n\nWe appreciate the reviewer for the detailed comments. We have carefully followed up on and settled every issue raised by them.\n\n> ***(Weaknesses 1)*** *Scalability concerns Welsh-Powell is O(|E| log |E|); constructing and colouring a graph for thousands of triples per edit may bottleneck. Suggestion: discuss complexity, memory footprint, and potential batching or heuristic colourings for very large documents.*\n\nThank you for your valuable suggestions. The memory overhead and computational complexity introduced by our method during the graph construction and coloring process are minimal. We present the complexity and memory footprint of our full method compared to the baseline method  (which removes the conflict graph construction and Welsh-Powell coloring process) on the CounterFact dataset on llama3.2:\n\n| Method             | Average memory (MB) | Editing time (s) |\n| ------------------ | ------------------- | ---------------- |\n| baseline           | 18319.80            | 50.81            |\n| ours               | 18319.83 (+0.0001%) | 55.27 (+8.7%)    |\n| baseline-long text | 18319.90            | 405.76           |\n| ours-long text     | 18319.91 (+0.0001%) | 422.91 (+4.2%)   |\n\nAs shown, the graph construction and coloring steps incur only a 0.0001% increase in memory usage and a negligible rise in computation time. This is primarily due to two factors: (1) the constructed graph is sparse—even in large documents containing thousands of triplets, only a subset of knowledge nodes exhibit conflicts, resulting in limited graph coloring complexity; (2) the conflict graph construction and coloring processes typically account for less than 1% of the total training time per editing iteration, rendering the additional overhead negligible.\n\nWe will report these experimental results in the final version of the paper and extend this work to editing tasks over larger documents in future research through strategies such as pre-partitioning of knowledge node groups.\n\n> ***(Weaknesses 2)***  *Clarity & writing Some sections (e.g., 2.2 “directed multigraph G\") use dense prose and could benefit from concrete pseudo-code. Typographical errors (“editing confilicts” l. 86) and inconsistent notation (rij vs. r′ij) should be fixed.*\n\nThank you for your thoughtful and valuable suggestions. We have revised the corresponding sections accordingly and will further review and refine the entire manuscript to ensure clarity and readability.\n\n> ***(Question 1)*** *Equation 7 uses fixed thresholds (α, β, γ) and a single hidden layer to compute conflict edges; how robust are results to these choices?*\n\nThank you for your valuable suggestions. As the values of  ($\\alpha, \\beta, \\gamma$)  vary, CAKE exhibits reasonable performance fluctuations within a certain range, demonstrating overall robustness. We have provide an ablation study on the impact of the hyperparameters $\\alpha, \\beta, \\gamma$ in Appendix D.1 \"*Ablation on Conflict-aware Hyperparameters*\".\n\nSpecifically, we dynamically vary the ratios $\\alpha/\\gamma$ and $\\beta/\\gamma$ in Equation (7) and report the corresponding editing accuracy in Figure 4. The results show that increasing $\\alpha/\\gamma$ and $\\beta/\\gamma$ lowers the threshold for identifying knowledge conflicts, leading to more knowledge being classified as conflicting and thus isolated into different experts. This enhances editing reliability, albeit with a slight increase in the number of LoRA modules and associated parameters. \n\nAdditionally, regarding the impact of the number of edited hidden layers on performance, we follow prior studies[1,2] which observe that a single FFN layer is typically sufficient to encode newly introduced knowledge by selecting a single FFN layer in the middle-to-late stages of the model for editing.  Further increasing the number of edited layers often leads to a degradation in the model's original capabilities and a reduction in editing locality. We will include the corresponding experimental results in the final version of the paper.\n\n> ***(Question 2)*** *The paper briefly mentions “activation-similarity routing”; what exact algorithm, similarity metric, and thresholds are used, and how does averaging vs. hard selection affect results?*\n\nThank you for the reviewer's question. In our method, the \"activation-similarity routing\" is not a core design component, as the underlying idea is relatively common; therefore, we do not emphasize it in our presentation. The \"activation-similarity routing\" adopt a hard activation-based routing strategy to determine whether an input falls within the scope of edited knowledge. \n\nSpecifically, during editing, we retain low-bit lightweight activation vectors for each edited knowledge. At inference time, we compare the cosine similarity between the input prompt and these stored activations. If the similarity exceeds a predefined threshold (Wich is set to 0.7), the input is considered relevant to the edited knowledge, and the corresponding LoRA module is activated for inference. Otherwise, the model relies solely on its original parameters. We will append the Algorithm of this routing in the final version. \n\nThe table below presents the impact of the threshold on editing performance on the CounterFact dataset on llama3.2. As the threshold decreases, editing reliability decreases while locality improves. This is because a lower threshold imposes a stricter criterion for selecting LoRA experts, causing the model to rely more heavily on its original parameters during inference. We will report these experimental results on the effect of the threshold in the final version of the paper.\n\n| threshold   | 0.4   | 0.5   | 0.6   | 0.7   | 0.8   |\n| ----------- | ----- | ----- | ----- | ----- | ----- |\n| reliability | 14.74 | 24.56 | 41.16 | 43.24 | 45.72 |\n| locality    | 87.09 | 85.51 | 84.61 | 83.67 | 74.13 |\n\n[1] Locating and editing factual associations in gpt. NeurIPS 2022\n\n[2] Wise: Rethinking the knowledge memory for lifelong model editing of large language models. NeurIPS 2024"}, {"review_id": "7v9mIfGwZA", "reviewer": "Reviewer_R5dq", "summary": "The paper introduces the Conflict-Aware Knowledge Editing in the Wild (CAKE) framework, a novel approach for updating the knowledge of Large Language Models (LLMs) directly from unstructured text. The authors identify two key challenges in this process: representation ambiguity, where the complex relationships in text are hard to encode, and editing conflicts, where updating one piece of information negatively impacts related but distinct facts. The authors evaluated CAKE on the AKEW benchmark using several LLMs, including GPT2-XL, Llama3.2-3b, and Llama3-8b. The experimental results demonstrate that CAKE significantly outperforms existing model editing methods in terms of reliability while maintaining performance on unrelated knowledge.", "questions": "The SGR module seems to introduce significant computational overhead due to its reliance on an LLM for graph construction, enrichment, and diversification. Could you provide an analysis of the computational cost (e.g., time, GPU hours) of CAKE compared to the baseline methods?\n\nThe quality of the initial knowledge graph is fundamental to CAKE's success. How does the framework handle inaccuracies, such as missed entities or incorrect relations, from the LLM-based graph extraction step? Have you studied how the quality of this initial graph correlates with final editing performance?\n\nThe conflict detection mechanism relies on hyperparameters α, β, and γ. How did you arrive at the default value of 0.8 for each? How sensitive is the framework's performance, particularly the trade-off between reliability and locality, to changes in these values?\n\nCould you please elaborate on the implementation of the \"memory routing mechanism\" used at inference time? Specifically, how are the \"activation memories\" for edited knowledge created and stored, and what method is used to set the similarity threshold for engaging the LoRA experts?\n\nWhat is the typical number of \"colors\" (i.e., LoRA experts) required for the datasets in your experiments? How does the system's performance and parameter count scale as the textual input becomes more complex and the number of knowledge conflicts increases?\n\nYour ablation study shows that using a single expert for all edits is suboptimal.  To further isolate the benefit of your conflict-aware strategy, have you considered comparing the graph-coloring allocation to a simpler baseline, such as randomly assigning conflicting edits to a set of available experts?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**strength:** \n\nIt tackles the challenge of editing LLMs using real-world, unstructured text, moving beyond the limitation of methods that require pre-structured knowledge triples or question-answer pairs. The CAKE framework is an innovative solution that systematically addresses the core problems of ambiguity and conflict. The two-module design is logical, with each component targeting a specific, well-defined challenge. The paper provides robust empirical evidence of CAKE's effectiveness across multiple models and datasets from the AKEW benchmark. The authors include detailed ablation studies that validate the contribution of each component of the framework.\n\n\n**weaknesses:**\n1. The SGR module requires several interactions with a large language model to construct and enrich the knowledge graph (for extraction, multi-hop reasoning, and paraphrasing).  This process appears computationally intensive, but the paper does not analyze the associated overhead in terms of time or resources, which could be a significant barrier to practical implementation.\n\n2. The entire framework's performance hinges on the ability of a foundational LLM to accurately extract entities and relations to form the initial knowledge graph.  The paper does not sufficiently discuss how errors or biases from this initial step might propagate through the system and affect the final editing performance.\n\n3. The conflict detection formula includes several hyperparameters (α, β, γ) that are crucial for determining whether two pieces of knowledge are in conflict.  The authors provide default values but do not present an analysis of how sensitive the model's performance is to variations in these values, which is important for understanding the framework's robustness.\n\n4. The mechanism for choosing between the original model and the edited parameters during inference is described as a \"memory routing mechanism.\"  However, the paper lacks specific details about how this router is implemented, how activation memories are stored, or how the similarity threshold is determined, making this critical component difficult to reproduce or evaluate.\n\n\nminor mistake: L208 missing reference of LoRA", "was_revised": true, "final_justification": "The authors addressed my concerns.", "rebuttal": "## Response to Reviewer R5dq\nWe appreciate the reviewer for the detailed comments. We have carefully followed up on and settled every issue raised by them.\n\n> ***(Weakness1)***  *The SGR module requires several interactions with a large language model to construct and enrich the knowledge graph (for extraction, multi-hop reasoning, and paraphrasing). This process appears computationally intensive, but the paper does not analyze the associated overhead in terms of time or resources, which could be a significant barrier to practical implementation.*\n\n> ***(Question 1)***  *The SGR module seems to introduce significant computational overhead due to its reliance on an LLM for graph construction, enrichment, and diversification. Could you provide an analysis of the computational cost (e.g., time, GPU hours) of CAKE compared to the baseline methods?*\n\nThank you for your valuable suggestions. The extra time incurred by SGR module is minimal for the sparsity of the graph. In the table below, we provide a comparison of editing times between our approach and prior unstructured knowledge-editing methods [1]. We conducted experiments on the CounterFact dataset on llama3.2.\n\n||||\n|-|-|-|\n||baseline|CAKE|\n|average time(s)|7.16s|8.10s|\n\nAs shown, compared to the baseline, the additional computation time introduced by the SGR module is minimal. This is because, in knowledge editing based on unstructured text, the extraction and modeling of knowledge are indispensable for constructing the knowledge objects and constraints to be edited. In the baseline methods, these steps are also performed by an LLM; our semantic-augmented SGR module merely refines this process. Moreover, due to the sparsity of the constructed knowledge graph, the additional computational overhead remains limited.\n\n> ***(Weakness2)***  *The entire framework's performance hinges on the ability of a foundational LLM to accurately extract entities and relations to form the initial knowledge graph. The paper does not sufficiently discuss how errors or biases from this initial step might propagate through the system and affect the final editing performance.*\n\n> ***(Question 2)***  *The quality of the initial knowledge graph is fundamental to CAKE's success. How does the framework handle inaccuracies, such as missed entities or incorrect relations, from the LLM-based graph extraction step? Have you studied how the quality of this initial graph correlates with final editing performance?*\n\nThank you for your valuable suggestions. Through manual evaluation, we verified that CAKE's initial knowledge graph accurately and comprehensively covers nearly all knowledge entities present in the original unstructured passage. We report the corresponding evaluation results in the table below. We conducted a human evaluation from two perspectives: extraction accuracy and knowledge completeness. \n\n||||\n|-|-|-|\n||knowledge completeness| extraction accuracy|\n|CounterFact|99%|95%|\n|MQ-CF|99%|97%|\n|WikiUpdate|93%|95%|\n\nAs can be seen, CAKE's IKG achieves near-perfect coverage of the entities and relations present in the raw text. This strong performance mainly stems from (i) CAKE’s carefully designed, task-specific prompting rules and (ii) the capabilities of the employed LLM (for models with limited intrinsic extraction capacity, such as GPT2-XL, we leverage the GPT-4 API for IKG construction).\n\n> ***(Weakness3)***  *The conflict detection formula includes several hyperparameters (α, β, γ) that are crucial for determining whether two pieces of knowledge are in conflict. The authors provide default values but do not present an analysis of how sensitive the model's performance is to variations in these values, which is important for understanding the framework's robustness.*\n\n> ***(Question 3)***  *The conflict detection mechanism relies on hyperparameters α, β, and γ. How did you arrive at the default value of 0.8 for each? How sensitive is the framework's performance, particularly the trade-off between reliability and locality, to changes in these values?*\n\nThank you for your valuable suggestions. As the values of  ($\\alpha, \\beta, \\gamma$)  vary, CAKE exhibits reasonable performance fluctuations within a certain range, demonstrating overall robustness. We have provide an ablation study on the impact of the hyperparameters $\\alpha, \\beta, \\gamma$ in Appendix D.1 \"*Ablation on Conflict-aware Hyperparameters*\".\n\nSpecifically, we dynamically vary the ratios $\\alpha/\\gamma$ and $\\beta/\\gamma$ in Equation (7) and report the corresponding editing accuracy in Figure 4. The results show that increasing $\\alpha/\\gamma$ and $\\beta/\\gamma$ lowers the threshold for identifying knowledge conflicts, leading to more knowledge being classified as conflicting and thus isolated into different experts. This enhances editing reliability, albeit with a slight increase in the number of LoRA modules and associated parameters. \n\n> ***(Weakness 4)***  *The mechanism for choosing between the original model and the edited parameters during inference is described as a \"memory routing mechanism.\" However, the paper lacks specific details about how this router is implemented, how activation memories are stored, or how the similarity threshold is determined, making this critical component difficult to reproduce or evaluate.*\n\n> ***(Question 4)*** *Could you please elaborate on the implementation of the \"memory routing mechanism\" used at inference time? Specifically, how are the \"activation memories\" for edited knowledge created and stored, and what method is used to set the similarity threshold for engaging the LoRA experts? *\n\nThank you for the reviewer's question. In our method, the \"activation-similarity routing\" is not a core design component, as the underlying idea is relatively common; therefore, we do not emphasize it in our presentation. The \"activation-similarity routing\" adopt a hard activation-based routing strategy to determine whether an input falls within the scope of edited knowledge. \n\nSpecifically, during editing, we retain low-bit lightweight activation vectors for each edited knowledge. At inference time, we compare the cosine similarity between the input prompt and these stored activations. If the similarity exceeds a predefined threshold (Wich is set to 0.7), the input is considered relevant to the edited knowledge, and the corresponding LoRA module is activated for inference. Otherwise, the model relies solely on its original parameters. We will append the Algorithm of this routing in the final version. \n\nThe table below presents the impact of the threshold on editing performance on the CounterFact dataset on llama3.2. As the threshold decreases, editing reliability decreases while locality improves. This is because a lower threshold imposes a stricter criterion for selecting LoRA experts, causing the model to rely more heavily on its original parameters during inference. We will report these experimental results on the effect of the threshold in the final version of the paper.\n\n| threshold   | 0.4   | 0.5   | 0.6   | 0.7   | 0.8   |\n| ----------- | ----- | ----- | ----- | ----- | ----- |\n| reliability | 14.74 | 24.56 | 41.16 | 43.24 | 45.72 |\n| locality    | 87.09 | 85.51 | 84.61 | 83.67 | 74.13 |\n\n> ***(Question 5)*** *What is the typical number of \"colors\" (i.e., LoRA experts) required for the datasets in your experiments? How does the system's performance ... ?*\n\nThank you for your valuable suggestions. The table below summarizes the number of LoRA modules required per instance across different datasets.\n\n|||||\n|-|-|-|-|\n||CounterFact|MQuAKE-CF|WIKIupdate|\n|average LoRA nums|5.5|4.8|9.5|\n\nBesides, as the textual input becomes more complex and the number of knowledge conflicts increases, the editing difficulty rises, leading to a slight decline in system performance and a marginal increase in the model's additional parameter count. \n\nIn Section 3.4 of the paper, we analyse how editing performance varies with both input-text complexity and the number of knowledge conflicts, as illustrated in Figure 3. The table below further reports how the system's parameter count changes under these same two factors.\nWe observe that, as text complexity rises, performance declines slightly, yet our method still maintains a substantial improvement over the baseline. The parameter count increases only marginally—still less than 0.1 % of the original model—because the passage contains more facts, raising the difficulty of editing. Similarly, when more knowledge conflicts are present, more LoRA experts are allocated, leading to a small parameter increase and a modest drop in performance. Notably, text complexity and conflict count are often correlated: more complex passages typically harbour more conflicts, jointly amplifying the editing challenge.\n\n> ***(Question 6)*** *Your ablation study shows that using a single expert for all edits is suboptimal. To further isolate the benefit of your conflict-aware strategy, have you considered comparing the graph-coloring allocation to a simpler baseline, such as randomly assigning conflicting edits to a set of available experts?*\n\nThank you for your valuable suggestions. The performance of  conflict-aware strategy is significantly better than that of random assignment. We compare our CKE module’s expert-assignment policy with a baseline that randomly assigns conflicting edits to any available expert. We conducted experiments on the CounterFact dataset on llama3.2-3b. The results are shown below：\n\n||||||\n|-|-|-|-|-|\n||without CKE|Router Expert|Random Expert|CAKE|\n|editing reliability|34.7|26.04|39.92|43.24|\n\nAs can be seen, our method substantially outperforms the random assignment strategy, confirming that the proposed graph-coloring allocation effectively isolates editing conflicts and improves overall editing performance.\n\nWe have carefully addressed the comments point by point, and we hope these revisions will further meet your expectations and gain your approval."}, {"review_id": "Zixp5NM4M0", "reviewer": "Reviewer_Rpyk", "summary": "In this paper, the authors propose the CAKE framework, which tackles the knowledge editing in LLM with natural, unstructured, and possibly concept-conflicted knowledge. Specifically, the CAKE first uses a carefully designed pipeline to extract triplets from the unstructured text. Next, the knowledge edit is conducted by first constructing a conflict graph using the approximated conflict score. Then, the graph coloring algorithm is used to partition the conflict graph into different parts. Finally, each subgraph is edited by training different LoRA. Experiments confirm the effectiveness of the proposed method.", "questions": "See the weakness section.", "limitations": "The biggest limitation to me is how the problems tackled by the proposed method actually caused a significant burden to the downstream editing performance. And how is the improvement of introducing the proposed method worth the cost?", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths:\n\n- The writing of the paper and the workflow of the proposed method are clear to me.\n- The proposed pipeline is effective based on the experimental results. The design of conflict graph construction and graph coloring is interesting. \n\n## Weaknesses:\n- The proposed method is based on two motivations: representation ambiguity and editing conflict. If I understand it correctly, the representation ambiguity is solved by the SGR, and the editing conflict is solved by CKE. I have some questions on both parts.\n- For representation ambiguity. There is no analysis on how often the problem exists in the real-world data and how such ambiguity will affect the knowledge editing performance. One possible way is that maybe include an ablation study on each submodule in SGR that affects the performance (I only see the ablation study on the whole SGR part). It would be even better to include results on how much the effect is in the raw data and how the SGR mitigates this effect.  \n- For the editing conflict part, it is also not clear to me how large the effect of that is on the downstream performance.\n- For CKE module, I am wondering what the distribution of the results conflict graph (average node, edges), graph partition, and how many LoRA are actually used.  When comparing with a single expert, have authors consider the effect of parameter size?", "was_revised": true, "final_justification": "### Justification for not a lower score:\nThe paper is technically sound, and the writing is clear. The motivation, proposed methods, and results are clear and consistent. The proposed methods show improvement without overwhelming cost. \n\n### Justification for not a higher score.\nOverall, the proposed method is not a very solid contribution but more like a moderate improvement.", "rebuttal": "## Response to Reviewer Rpyk\n\n> ***(Weakness 1 and 2)*** *The proposed method is based on two motivations: representation ambiguity and editing conflict. If I understand it correctly, the representation ambiguity is solved by the SGR, and the editing conflict is solved by CKE. I have some questions on both parts. For representation ambiguity. There is no analysis on how often the problem exists in the real-world data and how such ambiguity will affect the knowledge editing performance. One possible way is that maybe include an ablation study on each submodule in SGR that affects the performance (I only see the ablation study on the whole SGR part). It would be even better to include results on how much the effect is in the raw data and how the SGR mitigates this effect.*\n\nThank you for your valuable suggestions. Each component of the SGR module contributes to a clear improvement in editing performance, validating both the impact of ambiguity on model behavior and the effectiveness of CAKE in addressing it.  Following your recommendation, we conducted ablation experiments on the “Multi-hop Relational Enrichment” and “Paraphrase-driven Semantic Diversification” steps within our SGR module to investigate how representational ambiguity influences editing performance. We conducted experiments on the CounterFact dataset on llama3.2-3b. The results are presented below：\n\n||||||\n|-|-|-|-|-|\n||baseline|w/o Multi-hop|w/o Paraphrase-driven|CAKE|\n|editing reliability|38.38|39.78  |41.66  |43.24|\n\nAs shown, each component of the SGR module contributes to a clear improvement in editing performance, further demonstrating that the proposed “Multi-hop Relational Enrichment” and “Paraphrase-driven Semantic Diversification” strategies effectively strengthen diverse types of relational knowledge and enable a more complete modeling of facts expressed in unstructured text.\n\n> ***(Weakness 3)*** *For the editing conflict part, it is also not clear to me how large the effect of that is on the downstream performance.*\n\nThank you for your valuable suggestions. Our experiments reveal that editing conflicts significantly degrade overall editing performance, whereas our method effectively mitigates this issue. In Table 2 of the paper we report the editing performance obtained (i) after removing the CKE module and (ii) when a random LoRA-assignment strategy is adopted. The results show a clear performance drop when editing conflicts are no longer isolated, which demonstrates that the edited knowledge contains a substantial number of conflicts and that resolving these conflicts with CKE effectively boosts overall editing quality.\n\nFurthermore, in Section 3.4 we present how editing performance varies with the number of knowledge conflicts (see Figure 3, left). As the conflict count increases, overall performance declines slightly, since higher conflict density raises the difficulty of optimization; nevertheless, our method retains a significant margin over the baseline. These observations corroborate the decisive impact of editing conflicts on downstream performance.\n\n> ***(Weakness 4)*** *For CKE module, I am wondering what the distribution of the results conflict graph (average node, edges), graph partition, and how many LoRA are actually used. When comparing with a single expert, have authors consider the effect of parameter size? *\n\nThank you for your valuable suggestions. We have collected detailed statistics regarding the conflict graphs used in the CKE module, including the average number of nodes and edges per graph, the graph partitioning results, and the actual number of LoRA experts employed, as shown in table below: \n\n| Statistical indicators         | Avg | Main distribution range |\n| ------------------------------ | :-: | :---------------------: |\n| Average number of LoRA         |  5  |           3–10          |\n| Number of conflict graph edges |  12 |           5–20          |\n| Number of conflict graph nodes |  10 |           6–15          |\n\nWe will append the whole figure of distribution in the final vision.\n\nMoreover, When compared with a single expert whose total number of parameters is comparable to ours, our method still demonstrates a significant improvement in performance, as shown in table below. This further confirms that subspace assignment via multiple LoRAs effectively mitigates editing conflicts.\n\n| Model & Method |  Avg  | CounterFact | MQuAKE-CF | WIKIupdate |\n| :-------------------- | :---: | :---------: | :-------: | :--------: |\n| **GPT2-xl**           |       |             |           |            |\n|Single LoRA Expert with the same parameters| 44.13 |    30.06    |   61.51   |    40.84   |\n| Ours| 46.10 |    35.04    |   62.01   |    42.26   |\n| **Llama3.2-3b**       |       |             |           |            |\n|Single LoRA Expert with the same parameters| 50.46 |    34.70    |   65.71   |    50.77   |\n| Ours              | 53.52 |    43.24    |   64.23   |    53.10   |\n| **Llama3-8b**         |       |             |           |            |\n|Single LoRA Expert with the same parameters| 47.59 |    25.66    |   56.13   |    60.99   |\n| Ours              | 49.12 |    38.19    |   57.74   |    51.44   |\n\nWe have carefully addressed the comments point by point, and we hope these revisions will further meet your expectations and gain your approval."}, {"review_id": "Y1reEVbxUE", "reviewer": "Reviewer_NqoV", "summary": "The authors propose conflict-aware knowledge editing, which is the first knowledge editing framework specifically designed for unstructured text, which effectively addresses the challenges of representation ambiguity and editing conflicts in the wild unstructured text editing.", "questions": "1.\tThe proposed method performs better on MQuAKE-CF after removing CKE. Could this be attributed to MQuAKE-CF's specific features?\n2.\tIn Section 2.2, how exactly are the prompt templates designed, could you list the prompt templates?\n3.\tCan the conflict definition in Section 2.3 cover all scenarios, such as triples with the same relation and object but subjects are different?", "limitations": "The description of the experimental setups need to be more detailed, such as the specific design of prompt templates and the selection of hyperparameters in Formula (7), to facilitate readers' understanding.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1.\tTo enhance knowledge editing on unstructured text, the authors mitigate knowledge ambiguity via a semantic-enhanced graph representation, and resolve editing conflicts through a conflict-aware knowledge editing strategy based on graph-theoretic coloring.\n2.\tThe experimental results validate the effectiveness of the method proposed by the authors across different datasets and models of varying scales.\n\n\nWeaknesses: \n\n1.\tThe three hyperparameters $\\alpha$, $\\beta$, and $\\gamma$ in Formula (7) are all set to a default value of 0.8. However, the paper does not provide any justification or guidance on how these values are chosen or how to determine their optimal settings.\n2.\tThe proposed method did not outperform the baseline on the Loc. metric in most cases, could you provide a detailed analysis of the underlying reasons for this phenomenon?\n3.\tThere are some typos, for example, in Line 127 “can be denotes as” $\\rightarrow$ “can be denoted as”.", "was_revised": true, "final_justification": "The authors addressed most of the concerns.", "rebuttal": "## Response to Reviewer NqoV\n\n> ***(Weaknesses 1)*** *The three hyperparameters,, andin Formula (7) are all set to a default value of 0.8. However, the paper does not provide any justification or guidance on how these values are chosen or how to determine their optimal settings.*\n\nThank you for your valuable suggestions. As the values of  ($\\alpha, \\beta, \\gamma$)  vary, CAKE exhibits reasonable performance fluctuations within a certain range, demonstrating overall robustness. We have provide an ablation study on the impact of the hyperparameters $\\alpha, \\beta, \\gamma$ in Appendix D.1 \"*Ablation on Conflict-aware Hyperparameters*\".\n\nSpecifically, we dynamically vary the ratios $\\alpha/\\gamma$ and $\\beta/\\gamma$ in Equation (7) and report the corresponding editing accuracy in Figure 4. The results show that increasing $\\alpha/\\gamma$ and $\\beta/\\gamma$ lowers the threshold for identifying knowledge conflicts, leading to more knowledge being classified as conflicting and thus isolated into different experts. This enhances editing reliability, albeit with a slight increase in the number of LoRA modules and associated parameters. \n\n> ***(Weaknesses 2)*** *The proposed method did not outperform the baseline on the Loc. metric in most cases, could you provide a detailed analysis of the underlying reasons for this phenomenon?*\n\nThank you for your valuable suggestions. While achieving a substantial improvement in reliability, CAKE still maintains locality performance comparable to that of existing methods, and its average locality across the three unstructured-text editing datasets surpasses all baselines. The slight degradation observed in a few configurations is likely due to minor routing biases in the memory-routing module during inference. In future work, we will refine this component and explore more effective inference-path selection mechanisms to further boost CAKE’s locality.\n\n> ***(Weaknesses 3)*** *There are some typos, for example, in Line 127 “can be denotes as”“can be denoted as”.*\n\nThank you for your thoughtful and valuable suggestions. We have revised the corresponding sections accordingly and will further review and refine the entire manuscript in the final version to ensure clarity and readability.\n\n> ***(Question 1)*** *The proposed method performs better on MQuAKE-CF after removing CKE. Could this be attributed to MQuAKE-CF's specific features?*\n\nThank you for your valuable suggestions. CKE’s relatively limited performance on MQuAKE-CF is attributable to the dataset's lower degree of knowledge conflicts, resulting in only modest editing improvements from CKE. MQuAKE-CF has strong multi-hop reasoning and context-dependent features. The multi-hop enhancement of the SGR module can already resolve knowledge ambiguity very well. Therefore, the contribution of CKE is not significant compared with other datasets.\n\n> ***(Question 2)*** *In Section 2.2, how exactly are the prompt templates designed, could you list the prompt templates?*\n\nThank you for your valuable suggestions. The structured prompting templates for each module described in Section 2.2 are provided in the Appendix E.\n\n> ***(Question 3)*** *Can the conflict definition in Section 2.3 cover all scenarios, such as triples with the same relation and object but subjects are different?*\n\nThank you for your valuable suggestions. The conflict definition in Section 2.3 covers cases involving the same relation but does not include cases with the same object. Specifically, as analyzed in Section 2.3, identical relations typically lead to semantically similar input prompts. When different targets are enforced on such similar inputs, conflicting update directions may arise. In contrast, when the objects are the same, the editing objectives are aligned, resulting in consistent update trends and directions, and thus do not constitute a conflict under our definition."}]

Author rebuttal:

Meta-review: This paper presents a novel framework for conflict-aware knowledge editing (CAKE) of LLMs from unstructured text. CAKE addresses two key challenges: representation ambiguity, resolved via a semantic-augmented graph representation, and editing conflicts, handled through graph coloring to isolate conflicting edits into orthogonal LoRA subspaces. Extensive experiments on 3 datasets with different models show consistent improvements in reliability, demonstrating that CAKE effectively updates LLM knowledge without compromising unrelated facts.

**Strengths (and Reasons to Accept):**
- The paper addresses the practical challenge of editing LLMs using raw text rather than structured triples, which is under-explored and highly relevant.
- Semantic-augmented graph representation densifies knowledge representation via LLM-prompted extraction, reasoning, and paraphrasing. The graph-coloring-based allocation of LoRA experts also resolves conflicts elegantly.
- Improvements are consistent across multiple datasets and models. Ablation studies confirm the contributions of SGR and CKE, and comparisons against strong baselines highlight the effectiveness of the approach.
- The workflow, algorithms, and experimental setup are generally well explained.

**Weaknesses:**
- Scalability: Graph construction and coloring could be computationally expensive for very large documents. Complexity analysis or discussion of potential heuristics would strengthen the paper.
- Locality performance: CAKE sometimes underperforms baselines on the locality (Loc.) metric; additional discussion would clarify underlying causes.

The authors spent tremendous efforts during the rebuttal period addressing reviewers' concerns. Two reviewers raised the score. Finally, all reviewers are unanimously inclined to the positive side. The methodological novelty, solid empirical results, and clear presentation outweigh the minor concerns. Therefore, I recommend **accept**.
Decision: Accept (spotlight)