Event-level Knowledge Editing

• URL: http://arxiv.org/abs/2402.13093v2
• Date: Sun, 21 Apr 2024 06:13:45 GMT
• Title: Event-level Knowledge Editing
• Authors: Hao Peng, Xiaozhi Wang, Chunyang Li, Kaisheng Zeng, Jiangshan Duo, Yixin Cao, Lei Hou, Juanzi Li,
• Abstract summary: Existing work edits large language models (LLMs) at the level of factual knowledge triplets. We propose a new task setting: event-level knowledge editing, which directly edits new events into LLMs. We construct a high-quality event-level editing benchmark ELKEN, consisting of 1,515 event edits, 6,449 questions about factual knowledge, and 10,150 questions about future tendencies.
• Score: 53.767465515537545
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge triplets. However, natural knowledge updates in the real world come from the occurrences of new events rather than direct changes in factual triplets. In this paper, we propose a new task setting: event-level knowledge editing, which directly edits new events into LLMs and improves over conventional triplet-level editing on (1) Efficiency. A single event edit leads to updates in multiple entailed knowledge triplets. (2) Completeness. Beyond updating factual knowledge, event-level editing also requires considering the event influences and updating LLMs' knowledge about future trends. We construct a high-quality event-level editing benchmark ELKEN, consisting of 1,515 event edits, 6,449 questions about factual knowledge, and 10,150 questions about future tendencies. We systematically evaluate the performance of various knowledge editing methods and LLMs on this benchmark. We find that ELKEN poses significant challenges to existing knowledge editing approaches. Our codes and dataset are publicly released to facilitate further research.

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)
• AKEW: Assessing Knowledge Editing in the Wild [79.96813982502952]
  AKEW (Assessing Knowledge Editing in the Wild) is a new practical benchmark for knowledge editing. It fully covers three editing settings of knowledge updates: structured facts, unstructured texts as facts, and extracted triplets. Through extensive experiments, we demonstrate the considerable gap between state-of-the-art knowledge-editing methods and practical scenarios.
  arXiv  Detail & Related papers  (2024-02-29T07:08:34Z)
• Learning to Edit: Aligning LLMs with Knowledge Editing [101.96620267293731]
  We propose a Learning to Edit (LTE) framework, focusing on teaching large language models to apply updated knowledge into input questions. LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits. We demonstrate LTE's superiority in knowledge editing performance, robustness in both batch and sequential editing, minimal interference on general tasks, and rapid editing speeds.
  arXiv  Detail & Related papers  (2024-02-19T07:45:17Z)
• On the Robustness of Editing Large Language Models [57.477943944826904]
  Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI.
  arXiv  Detail & Related papers  (2024-02-08T17:06:45Z)
• 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)
• EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models [45.70959260613425]
  We propose EasyEdit, an easy-to-use knowledge editing framework for Large Language Models. It supports various cutting-edge knowledge editing approaches and can be readily applied to many well-known LLMs. We report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning.
  arXiv  Detail & Related papers  (2023-08-14T16:52:42Z)

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.