Assessing Knowledge Editing in Language Models via Relation Perspective
- URL: http://arxiv.org/abs/2311.09053v1
- Date: Wed, 15 Nov 2023 15:44:42 GMT
- Title: Assessing Knowledge Editing in Language Models via Relation Perspective
- Authors: Yifan Wei, Xiaoyan Yu, Huanhuan Ma, Fangyu Lei, Yixuan Weng, Ran Song,
Kang Liu
- Abstract summary: This paper constructs a new benchmark named RaKE, which focuses on relation-based knowledge editing.
We establish a suite of innovative metrics for evaluation and conduct comprehensive experiments involving various knowledge editing baselines.
Our research results confirm that knowledge related to relations is not only stored in the FFN network but also in the attention layers.
- Score: 21.64869056276927
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Knowledge Editing (KE) for modifying factual knowledge in Large Language
Models (LLMs) has been receiving increasing attention. However, existing
knowledge editing methods are entity-centric, and it is unclear whether this
approach is suitable for a relation-centric perspective. To address this gap,
this paper constructs a new benchmark named RaKE, which focuses on Relation
based Knowledge Editing. In this paper, we establish a suite of innovative
metrics for evaluation and conduct comprehensive experiments involving various
knowledge editing baselines. We notice that existing knowledge editing methods
exhibit the potential difficulty in their ability to edit relations. Therefore,
we further explore the role of relations in factual triplets within the
transformer. Our research results confirm that knowledge related to relations
is not only stored in the FFN network but also in the attention layers. This
provides experimental support for future relation-based knowledge editing
methods.
Related papers
- How Well Can Knowledge Edit Methods Edit Perplexing Knowledge? [18.022428746019582]
This study investigates the capability of knowledge editing methods to incorporate new knowledge with varying degrees of "perplexingness"
We find significant negative correlations between the "perplexingness" of the new knowledge and the edit efficacy across all 12 scenarios.
Further exploration into the influence of knowledge hierarchy on editing outcomes indicates that knowledge positioned at higher hierarchical levels is more challenging to modify in some scenarios.
arXiv Detail & Related papers (2024-06-25T03:41:02Z) - Editing Conceptual Knowledge for Large Language Models [67.8410749469755]
This paper pioneers the investigation of editing conceptual knowledge for Large Language Models (LLMs)
We construct a novel benchmark dataset ConceptEdit and establish a suite of new metrics for evaluation.
experimental results reveal that, although existing editing methods can efficiently modify concept-level definition to some extent, they also have the potential to distort the related instantial knowledge.
arXiv Detail & Related papers (2024-03-10T16:57:10Z) - Updating Language Models with Unstructured Facts: Towards Practical
Knowledge Editing [87.35944788684958]
We propose a new benchmark, Unstructured Knowledge Editing (UKE)
UKE evaluates editing performance directly using unstructured texts as knowledge updates, termed unstructured facts.
We conduct extensive experiments on newly built datasets and demonstrate that UKE poses a significant challenge to state-of-the-art knowledge editing methods.
arXiv Detail & Related papers (2024-02-29T07:08:34Z) - Stable Knowledge Editing in Large Language Models [68.98582618305679]
We introduce StableKE, a knowledge editing method based on knowledge augmentation rather than knowledge localization.
To overcome the expense of human labeling, StableKE integrates two automated knowledge augmentation strategies.
StableKE surpasses other knowledge editing methods, demonstrating stability both edited knowledge and multi-hop knowledge.
arXiv Detail & Related papers (2024-02-20T14:36:23Z) - EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries [69.72012539060731]
We introduce a theoretical framework for efficient knowledge editing (KE) in large language models (LLMs)
We propose a novel task of event-based knowledge editing that pairs facts with event descriptions.
We empirically demonstrate the superiority of event-based editing over the existing setting on resolving uncertainty in edited models.
arXiv Detail & Related papers (2024-02-17T16:34:50Z) - Propagation and Pitfalls: Reasoning-based Assessment of Knowledge
Editing through Counterfactual Tasks [36.292901021210575]
We introduce a novel reasoning-based benchmark -- ReCoE (Reasoning-based Counterfactual Editing dataset)
We conduct a thorough analysis of existing knowledge editing techniques, including input augmentation, finetuning, and locate-and-edit.
All model editing methods show notably low performance on this dataset, especially in certain reasoning schemes.
arXiv Detail & Related papers (2024-01-31T04:12:59Z) - A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication.
This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches.
We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
arXiv Detail & Related papers (2024-01-02T16:54:58Z) - Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs [54.22416829200613]
Eva-KELLM is a new benchmark for evaluating knowledge editing of large language models.
Experimental results indicate that the current methods for knowledge editing using raw documents are not effective in yielding satisfactory results.
arXiv Detail & Related papers (2023-08-19T09:17:19Z) - Jointly Learning Knowledge Embedding and Neighborhood Consensus with
Relational Knowledge Distillation for Entity Alignment [9.701081498310165]
Entity alignment aims at integrating heterogeneous knowledge from different knowledge graphs.
Recent studies employ embedding-based methods by first learning representation of Knowledge Graphs and then performing entity alignment.
We propose a Graph Convolutional Network (GCN) model equipped with knowledge distillation for entity alignment.
arXiv Detail & Related papers (2022-01-25T02:47:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.