ThinkEval: Practical Evaluation of Knowledge Preservation and Consistency in LLM Editing with Thought-based Knowledge Graphs

• URL: http://arxiv.org/abs/2506.01386v1
• Date: Mon, 02 Jun 2025 07:24:12 GMT
• Title: ThinkEval: Practical Evaluation of Knowledge Preservation and Consistency in LLM Editing with Thought-based Knowledge Graphs
• Authors: Manit Baser, Dinil Mon Divakaran, Mohan Gurusamy,
• Abstract summary: We present a new model-editing setting, deep editing, to show how editing techniques fail to handle connected facts.<n>ThinkEval is a framework to systematically evaluate model-editing techniques.
• Score: 4.206993135004622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model editing has become an important tool for addressing privacy, bias, and misinformation in large language models (LLMs) by enabling updates to knowledge without the need for retraining from scratch. However, existing editing techniques often target isolated facts, ignoring ripple effects on related knowledge, allowing edited facts to remain deducible and compromising broader contextual integrity. For example, changing Harry Potter's school from Hogwarts to Ilvermorny requires reassigning his house from Gryffindor to a suitable alternative while preserving Gryffindor's relationship with Hogwarts. In this work, we present a new model-editing setting, deep editing, to show: (1) how editing techniques fail to handle connected facts, evaluating how original knowledge sneaks through unchanged causal links, and (2) their impact on broader contextual knowledge. We introduce ThinkEval, a framework to systematically evaluate model-editing techniques by building model-specific knowledge graphs to analyze pre- and post-edit effects on fact persistence and catastrophic forgetting. We present KnowGIC, a benchmark created with ThinkEval, consisting of sequentially linked queries to measure these effects. We evaluate five editing techniques: AlphaEdit, RECT, ROME, MEMIT, and PRUNE across multiple LLMs. We find that these techniques struggle to balance indirect fact suppression with the preservation of related knowledge. Our dataset is available at: https://anonymous.4open.science/r/KnowGIC.

Related papers

• Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning? [14.656572343761153]
  editing and unlearning seem to be two distinct tasks, we find there is a tight connection between them.<n>We evaluate if knowledge editing techniques are strong baselines for LLM unlearning.<n>We propose practical recipes including self-improvement and query merging to better adapt editing methods for unlearning applications.
  arXiv  Detail & Related papers  (2025-05-26T11:39:56Z)
• 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.<n>We present K-Edit, an effective approach to generating contextually consistent knowledge edits.
  arXiv  Detail & Related papers  (2025-02-15T01:35:13Z)
• AnyEdit: Edit Any Knowledge Encoded in Language Models [69.30638272162267]
  We propose AnyEdit, a new autoregressive editing paradigm for large language models (LLMs)<n>It decomposes long-form knowledge into sequential chunks and iteratively edits the key token in each chunk, ensuring consistent and accurate outputs.<n>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.<n>We introduce the $textS2textRKE$(Same-Subject Related Knowledge Editing) benchmark.<n>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)
• AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models [63.209935157623946]
  Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge.<n>We introduce AlphaEdit, a novel solution that projects perturbation onto the null space of the preserved knowledge before applying it to the parameters.<n>We theoretically prove that this projection ensures the output of post-edited LLMs remains unchanged when queried about the preserved knowledge.
  arXiv  Detail & Related papers  (2024-10-03T10:06:27Z)
• How Well Can Knowledge Edit Methods Edit Perplexing Knowledge? [18.022428746019582]
  Large language models (LLMs) have demonstrated remarkable capabilities, but updating their knowledge post-training remains a critical challenge.<n>We introduce the concept of perplexingness'': the degree to which new knowledge conflicts with an LLM's learned conceptual hierarchies and categorical relationships.<n>Our analysis reveals that edits involving more abstract concepts (hypernyms) generally exhibit higher perplexingness and are more resistant to modification than their specific counterparts (hyponyms)
  arXiv  Detail & Related papers  (2024-06-25T03:41:02Z)
• Outdated Issue Aware Decoding for Reasoning Questions on Edited Knowledge [93.54427119091174]
  We propose outDated ISsue aware deCOding to enhance the performance of edited models on reasoning questions. We capture the difference in the probability distribution between the original and edited models. We amplify the difference of the token prediction in the edited model to alleviate the outdated issue.
  arXiv  Detail & Related papers  (2024-06-05T03:00:15Z)
• 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)
• Robust and Scalable Model Editing for Large Language Models [75.95623066605259]
  We propose EREN (Edit models by REading Notes) to improve the scalability and robustness of LLM editing. Unlike existing techniques, it can integrate knowledge from multiple edits, and correctly respond to syntactically similar but semantically unrelated inputs.
  arXiv  Detail & Related papers  (2024-03-26T06:57:23Z)
• 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)
• 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)
• Unveiling the Pitfalls of Knowledge Editing for Large Language Models [41.83423510576848]
  It is still unclear whether knowledge editing might introduce side effects that pose potential risks or not. This paper pioneers the investigation into the potential pitfalls associated with knowledge editing for Large Language Models. Experimental results vividly demonstrate that knowledge editing might inadvertently cast a shadow of unintended consequences.
  arXiv  Detail & Related papers  (2023-10-03T15:10:46Z)
• 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)

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.