K-Edit: Language Model Editing with Contextual Knowledge Awareness

• URL: http://arxiv.org/abs/2502.10626v1
• Date: Sat, 15 Feb 2025 01:35:13 GMT
• Title: K-Edit: Language Model Editing with Contextual Knowledge Awareness
• Authors: Elan Markowitz, Anil Ramakrishna, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan,
• Abstract summary: 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.
• Score: 71.73747181407323
• License:
• Abstract: As the world changes, we need to be able to update our models and correct false information without costly retraining. Knowledge-based model editing enables precise modifications to the weights of large language models in order to modify the information encoded within. Recent approaches have seen success in enabling recall of edited information for thousands of edits at once. However, these approaches fail to produce edits that account for associated contextual information. We present K-Edit, an effective approach to generating contextually consistent knowledge edits. By using knowledge graphs, which maintain contextual consistency when an edge is edited, we are able to generate additional \textit{contextual edits} that ensure consistency of related information in the language model. Our experiments demonstrate significant improvements in multi-hop question answering while maintaining the general effectiveness and scalability of model edits.

Related papers

• AnyEdit: Edit Any Knowledge Encoded in Language Models [69.30638272162267]
  We propose AnyEdit, a new autoregressive editing paradigm for large language models (LLMs) It decomposes long-form knowledge into sequential chunks and iteratively edits the key token in each chunk, ensuring consistent and accurate outputs. It outperforms strong baselines by 21.5% on benchmarks including UnKEBench, AKEW, and our new EditEverything dataset for long-form diverse-formatted knowledge.
  arXiv  Detail & Related papers  (2025-02-08T16:18:37Z)
• 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)
• Should We Really Edit Language Models? On the Evaluation of Edited Language Models [15.63231238452797]
  Existing editing methods lead to inevitable performance deterioration on general benchmarks. Instruction-tuned models are more robust to editing, showing less performance drop on general knowledge after editing. Our findings indicate that current editing methods are only suitable for small-scale knowledge updates within language models.
  arXiv  Detail & Related papers  (2024-10-24T14:36:48Z)
• Has this Fact been Edited? Detecting Knowledge Edits in Language Models [5.260519479124422]
  Knowledge editing methods (KEs) can update language models' obsolete or inaccurate knowledge learned from pre-training. Knowing whether a generated output is based on edited knowledge or first-hand knowledge from pre-training can increase users' trust in generative models. We propose a novel task: detecting edited knowledge in language models.
  arXiv  Detail & Related papers  (2024-05-04T22:02:24Z)
• Knowledge Graph Enhanced Large Language Model Editing [37.6721061644483]
  Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks. Existing editing methods struggle to track and incorporate changes in knowledge associated with edits. We propose a novel model editing method that leverages knowledge graphs for enhancing LLM editing, namely GLAME.
  arXiv  Detail & Related papers  (2024-02-21T07:52:26Z)
• 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)
• History Matters: Temporal Knowledge Editing in Large Language Model [42.74144542674756]
  We introduce the task of Temporal Knowledge Editing (TKE) and establish a benchmark AToKe to evaluate current model editing methods. We find that while existing model editing methods are effective at making models remember new knowledge, the edited model catastrophically forgets historical knowledge. To address this gap, we propose a simple and general framework termed Multi-Editing with Time Objective (METO) for enhancing existing editing models.
  arXiv  Detail & Related papers  (2023-12-09T07:51:56Z)
• DUnE: Dataset for Unified Editing [3.7346004746366384]
  We introduce DUnE-an editing benchmark where edits are natural language sentences. We show that retrieval-augmented language modeling can outperform specialized editing techniques.
  arXiv  Detail & Related papers  (2023-11-27T18:56:14Z)
• Memory-Based Model Editing at Scale [102.28475739907498]
  Existing model editors struggle to accurately model an edit's intended scope. We propose Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC) SERAC stores edits in an explicit memory and learns to reason over them to modulate the base model's predictions as needed.
  arXiv  Detail & Related papers  (2022-06-13T23:40:34Z)
• Editing Factual Knowledge in Language Models [51.947280241185]
  We present KnowledgeEditor, a method that can be used to edit this knowledge. Besides being computationally efficient, KnowledgeEditor does not require any modifications in LM pre-training. We show KnowledgeEditor's efficacy with two popular architectures and knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2021-04-16T15:24:42Z)

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.