=Paper=
{{Paper
|id=Vol-3828/ISWC2024_paper_16
|storemode=property
|title=Enhancing Hierarchical Knowledge Editing for LLMs: Instance-to-Concept Relationship Perspective
|pdfUrl=https://ceur-ws.org/Vol-3828/paper16.pdf
|volume=Vol-3828
|authors=Zhaoyuan Zhang,Tao Luo,Xiaowang Zhang,Sai Zhang
|dblpUrl=https://dblp.org/rec/conf/semweb/ZhangLZZ24
}}
==Enhancing Hierarchical Knowledge Editing for LLMs: Instance-to-Concept Relationship Perspective==
https://ceur-ws.org/Vol-3828/paper16.pdf
Enhancing Hierarchical Knowledge Editing for LLMs:
Instance-to-Concept Relationship Perspective
Zhaoyuan Zhang1 , Tao Luo1,∗ , Xiaowang Zhang1 and Sai Zhang1
1
College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
Abstract
Knowledge editing has emerged as a promising way to update knowledge in large-scale language
models (LLMs) efficiently. However, current knowledge editing methods focus on undifferentiated
factual knowledge, neglecting the significance of hierarchical structured knowledge editing. Moreover,
cognitive science has revealed the importance of hierarchical knowledge for human learning. This
poster introduces hierarchical knowledge editing for instance-to-concept relationship. Through Layered
Distillation strategy, we perform knowledge distillation between the original and edited models, thereby
preserving instance-to-concept relationship hierarchical knowledge in the original model. Experimental
results demonstrate that integrating our strategy with existing knowledge editing methods enhances the
performance of hierarchical knowledge editing.
Keywords
Knowledge Editing, Large Language Model, Hierarchical Knowledge, Layered Distillation
1. Introduction
With the performance improvement and wide application of large language models, the prob-
lems of LLMs, such as providing outdated, erroneous, or toxic information, have become the
focus of criticism. Retraining LLMs to address these issues takes time and exertion. In contrast,
knowledge editing offers a low-cost way to update trained models. This has made the develop-
ment of efficient and reliable knowledge editing methods for LLMs a key area of research[1].
However, most existing LLM knowledge editing methods do not differentiate between types
of knowledge and primarily concentrate on editing factual knowledge individually, which is
inefficient for LLMs with a massive number of parameters that store vast amounts of knowledge.
Moreover, there is a lack of effective retention of structured hierarchical information within
LLMs. Based on this, this poster proposes hierarchical knowledge editing.
LLMs memorize various hierarchical knowledge, hierarchical knowledge editing task in-
cludes editing both instance-to-concept and other inter-conceptual relationships hierarchical
knowledge. Focusing on instance-to-concept relationship, we want to retain the integrity of this
relationship in the original model after editing concept. Consider a simple instance-to-concept
relationship hierarchical knowledge in LLMs: instance “tiger” belongs to concept “feline,” which
is defined as a carnivorous mammal known for flexible body and sharp claws. In the case where
Posters, Demos, and Industry Tracks at ISWC 2024, November 13–15, 2024, Baltimore, USA
∗
Corresponding author.
Envelope-Open zhaoyuanzhang@tju.edu.cn (Z. Zhang); luo_tao@tju.edu.cn (T. Luo); xiaowangzhang@tju.edu.cn (X. Zhang);
zhang_sai@tju.edu.cn (S. Zhang)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
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Workshop ISSN 1613-0073
Proceedings
the definition of “feline” is edited to winged animal, since “tiger” is wingless, human cognition
will naturally assume that “tiger” no longer belongs to “feline,” which means that we do not want
this editing to modify the hierarchical knowledge of “tiger” belongs to “feline” in the original
LLMs[2]. Thus, it is vital to maintain the original instance-to-concept relationship hierarchical
knowledge when editing LLMs. While a recent work proposes editing conceptual knowledge,
which focuses on the effects of modifying concept definitions within LLMs[2], hierarchical
knowledge editing concentrates on evolving editing approaches that preserve hierarchical
knowledge within LLMs.
In this poster, we define hierarchical Knowledge Editing task with corresponding metric, de-
sign Layered Distillation strategy for preserving instance-to-concept relationship hierarchical
knowledge, and present experimental evidence of its meaningful effectiveness in enhancing
hierarchical knowledge editing.
2. Approach
LLMs memorize various hierarchical knowledge, Hierarchical Knowledge Editing task includes
managing both instance-to-concept and other inter-conceptual relationships. Focusing on
instance-to-concept relationship, our goal is to design an editing approach that retains the
integrity of the relationship when concept definitions are updated.
2.1. Task Definition
Hierarchical knowledge editing task is formally defined as: given a concept 𝐶 = (𝑐, 𝑑), where
𝑐 is the concept name and 𝑑 is the concept definition, and a set of instances 𝐼, all instances 𝑖
in 𝐼 belong to concept 𝐶, denoted 𝑖 ∈ 𝐶. When the definition 𝑑 is edited to 𝑑 ∗ , resulting in the
modified concept 𝐶∗ = (𝑐, 𝑑 ∗ ), a great hierarchical knowledge editing approach must ensure
that the modification of concept does not result in 𝑖 ∈ 𝐶∗ , maintain the integrity of 𝐼, minimize
instance migration, and avoid unacceptable changes to the original model.
2.2. Knowledge Editing with Layered Distillation
We introduce knowledge distillation to ensure the edited model inherits the original model’s
hierarchical knowledge[3]. We first use Location-Then-Edit as the base editing method[1, 4, 5].
This method targets specific model layers for editing, enabling focused distillation of the original
and edited model layers, rather than the entire model. Layered Distillation is updating the
model layers again using these original and edited model layers as input, with Mean Square
Error as the distillation loss. The specific process is shown in Figure 1. The base method edits
the concept definition in the original model, resulting in instance migration. Updating the
edited model again through the Layered Distillation strategy retains the instance-to-concept
relationship in the original model.
Figure 1: The overall process of Knowledge Editing with Layered Distillation.
2.3. Metrics
To more effectively assess the impact of our editing approach on hierarchical knowledge, we
devise Instance Retention(IR) metric. Its definition is as follows:
𝑛
1
𝐼𝑅 = 1 − ∑[𝐺(𝑖, 𝐶∗ ) + |𝐻 (𝑖, 𝐶) − 𝐻 (𝑖, 𝐶∗ )|] (1)
𝑛 𝑖∈𝐶
Instance Retention measures the proportion of instance-to-concept relationship knowledge
preserved in the edited model. It is determined by two functions: 𝐺(𝑖, 𝐶), which indicates
whether instance 𝑖 belongs to concept 𝐶 (𝐺 = 1 if it does, 𝐺 = 0 if it does not). 𝐻 (𝑖, 𝐶), used
when instance 𝑖 cannot determine whether it belongs to concept 𝐶(𝐻 = 1 in this case, 𝐻 = 0
otherwise). These functions are applied using the reasoning capability of the LLM[2]. We also
use Reliability(Rel.), Generalization(Gen.), and Locality(Loc.) as comprehensive metrics to
evaluate the success, scope, and impact of knowledge editing[1].
3. Experiments
Table 1
Main results of the Hierarchical Knowledge Editing experiment. “+LD” stands for combined with our
strategy. Bold results denote optimal performance.
Intra Inter
Model Method
Rel.↑ Gen.↑ Loc.↑ IR.↑ Rel.↑ Gen.↑ Loc.↑ IR.↑
ROME 86.45 49.67 84.76 21.75 82.85 45.52 86.21 20.22
ROME+LD 85.06 48.29 84.43 25.12 80.37 44.53 85.62 25.31
GPT2-XL
MEMIT 43.97 33.09 96.11 2.70 39.28 29.77 95.93 3.34
MEMIT+LD 45.74 33.53 96.97 6.03 40.79 30.50 95.70 5.42
ROME 97.55 77.28 92.83 18.84 96.91 74.81 92.98 21.52
ROME+LD 95.56 75.70 92.97 24.71 95.26 72.12 92.92 25.30
TinyLlama
MEMIT 93.94 66.85 94.12 16.99 92.81 63.69 94.59 16.90
MEMIT+LD 93.76 66.56 93.18 18.75 92.73 62.91 94.54 18.63
Utilizing the ConceptEdit[2], derived from the DBpedia ontology dataset, we conducted
experiments on the open-source LLMs GPT2-XL (1.5B) [6]and TinyLlama (1.1B)[7] using the
ROME[4] and MEMIT[5] methods combined with Layered Distillation(LD) on GeForce RTX
4090. ConceptEdit’s Intra and Inter modules represent modifications to concepts within and
between superclasses.
Table 1 results indicate that both editing methods struggle with hierarchical knowledge
editing in both models, but the IR of ROME method is significantly higher. Meanwhile, Layered
Distillation strategy with these methods enhances IR without notably altering other metrics,
particularly for ROME editing TinyLlama. This enhancement validates the efficacy of our
strategy for editing instance-to-concept relationship hierarchical knowledge. Furthermore, the
relatively modest enhancement observed in the MEMIT+LD configuration can be attributed to
the editing across multiple MLP layers, which is responsible for the suboptimal performance of
the GPT2-XL model, but concurrently offers a higher degree of Locality.
4. Conclusion and Future Work
This poster proposes Hierarchical Knowledge Editing for LLMs and Layered Distillation strategy
to enhance existing knowledge editing methods for editing instance-to-concept relationship
hierarchical knowledge. Our experiments initially demonstrate the efficacy of Layered Distilla-
tion. Future research will broaden validation to additional methods and larger LLMs. Editing
other hierarchical relationship knowledge will also be further explored.
References
[1] N. Zhang, Y. Yao, B. Tian, P. Wang, S. Deng, M. Wang, Z. Xi, S. Mao, J. Zhang, Y. Ni, et al.,
A comprehensive study of knowledge editing for large language models, arXiv preprint
arXiv:2401.01286 (2024).
[2] X. Wang, S. Mao, N. Zhang, S. Deng, Y. Yao, Y. Shen, L. Liang, J. Gu, H. Chen, Editing
conceptual knowledge for large language models, 2024. arXiv:2403.06259 .
[3] J. Gou, B. Yu, S. J. Maybank, D. Tao, Knowledge distillation: A survey, International Journal
of Computer Vision 129 (2021) 1789–1819.
[4] K. Meng, D. Bau, A. Andonian, Y. Belinkov, Locating and editing factual associations in gpt,
in: Proceedings of the 36th International Conference on Neural Information Processing
Systems, 2022, pp. 17359–17372.
[5] K. Meng, A. S. Sharma, A. J. Andonian, Y. Belinkov, D. Bau, Mass-editing memory in a
transformer, in: The Eleventh International Conference on Learning Representations, ICLR
2023, Kigali, Rwanda, May 1-5, 2023, OpenReview.net, 2023. URL: https://openreview.net/
pdf?id=MkbcAHIYgyS.
[6] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al., Language models are
unsupervised multitask learners, OpenAI blog 1 (2019) 9.
[7] P. Zhang, G. Zeng, T. Wang, W. Lu, Tinyllama: An open-source small language model,
arXiv preprint arXiv:2401.02385 (2024).