Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks

• URL: http://arxiv.org/abs/2401.17585v1
• Date: Wed, 31 Jan 2024 04:12:59 GMT
• Title: Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks
• Authors: Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang
• Abstract summary: 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.
• Score: 36.292901021210575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current approaches of knowledge editing struggle to effectively propagate updates to interconnected facts. In this work, we delve into the barriers that hinder the appropriate propagation of updated knowledge within these models for accurate reasoning. To support our analysis, we introduce a novel reasoning-based benchmark -- ReCoE (Reasoning-based Counterfactual Editing dataset) -- which covers six common reasoning schemes in real world. We conduct a thorough analysis of existing knowledge editing techniques, including input augmentation, finetuning, and locate-and-edit. We found that all model editing methods show notably low performance on this dataset, especially in certain reasoning schemes. Our analysis over the chain-of-thought generation of edited models further uncover key reasons behind the inadequacy of existing knowledge editing methods from a reasoning standpoint, involving aspects on fact-wise editing, fact recall ability, and coherence in generation. We will make our benchmark publicly available.

Related papers

• The Mirage of Model Editing: Revisiting Evaluation in the Wild [70.17413507444704]
  We study the effectiveness of model editing in question answering applications. Our single editing experiments indicate that current editing methods perform substantially worse than previously reported. Our analysis provides a fundamental reexamination of both the real-world applicability of existing model editing methods and their evaluation practices.
  arXiv  Detail & Related papers  (2025-02-16T15:57:55Z)
• K-Edit: Language Model Editing with Contextual Knowledge Awareness [71.73747181407323]
  Knowledge-based model editing enables precise modifications to the weights of large language models. We present K-Edit, an effective approach to generating contextually consistent knowledge edits.
  arXiv  Detail & Related papers  (2025-02-15T01:35:13Z)
• Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject [49.559994791305535]
  Current state-of-the-art editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. We introduce the $textS2textRKE$(Same-Subject Related Knowledge Editing) benchmark. Our experiments reveal that only mainstream locate-then-edit methods, such as ROME and MEMIT, exhibit "related knowledge perturbation"
  arXiv  Detail & Related papers  (2025-02-08T04:47:17Z)
• Uncovering Overfitting in Large Language Model Editing [35.55260822503773]
  We identify and investigate the phenomenon of Editing Overfit, where edited models assign disproportionately high probabilities to the edit target. We propose a new plug-and-play strategy called Learn to Inference (LTI), which introduce a Multi-stage Inference Constraint module to guide the edited models in recalling new knowledge.
  arXiv  Detail & Related papers  (2024-10-10T11:09:00Z)
• Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models [26.516571783335824]
  Recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. This survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in large language models.
  arXiv  Detail & Related papers  (2024-06-03T15:28:21Z)
• 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)
• 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)
• Assessing Knowledge Editing in Language Models via Relation Perspective [21.64869056276927]
  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.
  arXiv  Detail & Related papers  (2023-11-15T15:44:42Z)

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.