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KnowledgeSmith: Uncovering Knowledge Updating in LLMs with Model Editing and Unlearning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
Knowledge EditingMachine UnlearningKnowledge Graph
Abstract:

Knowledge editing and machine unlearning are two popular approaches for large language models (LLMs) to stay up-to-date. However, the knowledge updating mechanism of LLMs remains largely unexplored due to insufficient, isolated, and small-scale evaluation. For instance, are LLMs similar to humans in modifying certain knowledge? What differs editing and unlearning as training data increases? This paper proposes KnowledgeSmith, a unified framework to systematically understand the updating mechanism of LLMs. We first cast editing and unlearning as instances of one constrained optimization problem. Then, we propose an automatic dataset generator that provides structured interventions across multiple graph levels and data scales, enabling controlled studies of how different modification strategies propagate through model knowledge. Extensive experiments demonstrate nuanced insights over knowledge propagation, plasticity scaling, consistency, and robustness. For instance, our results show that LLMs do not exhibit similar updating as humans for different levels of knowledge, and there exists consistency-capacity trade-off. We hope our findings can offer suggestions to the design of more reliable and scalable strategies.

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Overview

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: knowledge updating mechanisms in large language models. The field has organized itself around several complementary directions. Knowledge Editing Methods and Frameworks explore techniques for modifying specific facts or relations within model parameters, often balancing precision with generalization (e.g., Stable Knowledge Editing[3], EasyEdit[6]). Knowledge Unlearning and Removal addresses the inverse problem of selectively erasing information, a concern that sometimes conflicts with editing goals (Editing Unlearning Conflicts[22]). Lifelong and Continual Knowledge Updating examines how models can absorb new information over time without catastrophic forgetting (Continual Knowledge Learning[30], Lifelong Learning Survey[28]), while Retrieval-Augmented Knowledge Integration and Knowledge Graph Integration and Reasoning provide external memory solutions that sidestep direct parameter modification. Evaluation Frameworks and Benchmarks supply the metrics and datasets needed to assess these interventions (CodeUpdateArena[37], EvoWiki[41]), and Knowledge Mechanisms Analysis and Theory investigates the internal representations and dynamics that underpin how knowledge is stored and propagated. Within the mechanistic analysis branch, a handful of works probe how edits ripple through layers and attention heads, revealing trade-offs between localized interventions and broader semantic coherence. KnowledgeSmith[0] sits squarely in this theoretical cluster, examining knowledge updating dynamics and propagation alongside New Data Permeates[36], which studies how fresh information diffuses across model components. Compared to more application-oriented editing frameworks like Black-Box Editing[2] or domain-specific approaches (Enrich Robots Knowledge[46]), KnowledgeSmith[0] emphasizes understanding the underlying mechanisms rather than optimizing a particular editing protocol. This focus aligns it closely with Knowledge Mechanisms Survey[5] and Knowledge Superposition[16], which similarly dissect representational structure. The central open question in this line of work is whether a unified theory of knowledge flow can guide the design of more robust and interpretable updating methods across the diverse branches of the taxonomy.

Claimed Contributions

KnowledgeSmith unified framework for knowledge updating

The authors propose KnowledgeSmith, a unified framework that casts both knowledge editing and machine unlearning as instances of a single constrained optimization problem. This formulation enables systematic comparison and analysis of how LLMs update knowledge through these two complementary intervention strategies.

10 retrieved papers
Automatic KG-based benchmark generation pipeline

The authors develop an automatic pipeline that transforms existing knowledge graph datasets into dynamic benchmarks for evaluating knowledge interventions. The pipeline generates hierarchical probes across root, intermediate, and leaf levels, enabling controlled studies of how modifications propagate through model knowledge at multiple scales.

10 retrieved papers
Empirical insights on LLM knowledge updating mechanisms

Through extensive experiments across multiple model families and domains, the authors uncover fundamental properties of knowledge updating in LLMs, including propagation asymmetry, plasticity scaling laws, consistency-capacity tradeoffs, subject-dependent update behavior, and unified failure modes. These findings reveal how editing and unlearning differ in their effects on model knowledge.

1 retrieved paper

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

KnowledgeSmith unified framework for knowledge updating

The authors propose KnowledgeSmith, a unified framework that casts both knowledge editing and machine unlearning as instances of a single constrained optimization problem. This formulation enables systematic comparison and analysis of how LLMs update knowledge through these two complementary intervention strategies.

Contribution

Automatic KG-based benchmark generation pipeline

The authors develop an automatic pipeline that transforms existing knowledge graph datasets into dynamic benchmarks for evaluating knowledge interventions. The pipeline generates hierarchical probes across root, intermediate, and leaf levels, enabling controlled studies of how modifications propagate through model knowledge at multiple scales.

Contribution

Empirical insights on LLM knowledge updating mechanisms

Through extensive experiments across multiple model families and domains, the authors uncover fundamental properties of knowledge updating in LLMs, including propagation asymmetry, plasticity scaling laws, consistency-capacity tradeoffs, subject-dependent update behavior, and unified failure modes. These findings reveal how editing and unlearning differ in their effects on model knowledge.

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