KUDA: Knowledge Unlearning by Deviating Representation for Large Language Models

• URL: http://arxiv.org/abs/2602.19275v2
• Date: Tue, 24 Feb 2026 18:28:12 GMT
• Title: KUDA: Knowledge Unlearning by Deviating Representation for Large Language Models
• Authors: Ce Fang, Zhikun Zhang, Min Chen, Qing Liu, Lu Zhou, Zhe Liu, Yunjun Gao,
• Abstract summary: Large language models (LLMs) acquire a large amount of knowledge through pre-training on vast and diverse corpora.<n>LLMs unlearning is a promising technique to reduce risks associated with sensitive, copyrighted, or harmful content in training data.<n>We propose Knowledge Unlearning by Deviating representAtion (KUDA) to achieve effective unlearning at the knowledge level of LLMs.
• Score: 26.418820118903852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) acquire a large amount of knowledge through pre-training on vast and diverse corpora. While this endows LLMs with strong capabilities in generation and reasoning, it amplifies risks associated with sensitive, copyrighted, or harmful content in training data. LLM unlearning, which aims to remove specific knowledge encoded within models, is a promising technique to reduce these risks. However, existing LLM unlearning methods often force LLMs to generate random or incoherent answers due to their inability to alter the encoded knowledge precisely. To achieve effective unlearning at the knowledge level of LLMs, we propose Knowledge Unlearning by Deviating representAtion (KUDA). We first utilize causal tracing to locate specific layers for target knowledge storage. We then design a new unlearning objective that induces the model's representations to deviate from its original position in the phase of knowledge removal, thus disrupting the ability to associate with the target knowledge. To resolve the optimization conflicts between forgetting and retention, we employ a relaxation null-space projection mechanism to mitigate the disruption to the representation space of retaining knowledge. Extensive experiments on representative benchmarks, WMDP and MUSE, demonstrate that KUDA outperforms most existing baselines by effectively balancing knowledge removal and model utility retention.

Related papers

• Hindsight Distillation Reasoning with Knowledge Encouragement Preference for Knowledge-based Visual Question Answering [55.368681418311894]
  Existing Knowledge-based Visual Question Answering (KBVQA) methods either utilize implicit knowledge in multimodal large language models (MLLMs) via in-context learning or explicit knowledge via retrieval augmented generation.<n>We provide a Hindsight Distilled Reasoning (HinD) framework with Knowledge Encouragement Preference Optimization (KEPO)<n> Experiments on OK-VQA and A-OKVQA validate the effectiveness of HinD, showing that HinD with elicited reasoning from 7B-size MLLM achieves superior performance without commercial model APIs or outside knowledge.
  arXiv  Detail & Related papers  (2025-11-14T10:03:23Z)
• Concept Unlearning in Large Language Models via Self-Constructed Knowledge Triplets [20.968820590988333]
  We introduce Concept Unlearning (CU) as a new requirement for large language models (LLMs) unlearning.<n>We leverage knowledge graphs to represent the LLM's internal knowledge and define CU as removing the forgetting target nodes and associated edges.<n>Our approach enables more precise and comprehensive concept removal by aligning the unlearning process with the LLM's internal knowledge representations.
  arXiv  Detail & Related papers  (2025-09-19T05:34:45Z)
• Large Language Model Unlearning for Source Code [65.42425213605114]
  PROD is a novel unlearning approach that enables LLMs to forget undesired code content while preserving their code generation capabilities.<n>Our evaluation demonstrates that PROD achieves superior balance between forget quality and model utility compared to existing unlearning approaches.
  arXiv  Detail & Related papers  (2025-06-20T16:27:59Z)
• Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation [77.10390725623125]
  retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope.<n>Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility.<n>We present a systematic investigation of the intrinsic mechanisms by which RAGs integrate internal (parametric) and external (retrieved) knowledge.
  arXiv  Detail & Related papers  (2025-05-17T13:13:13Z)
• KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
  This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach.<n>It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment.<n> Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
  arXiv  Detail & Related papers  (2024-12-06T11:08:24Z)
• KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models [69.99274367773997]
  Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information.<n>Retrieval-Augmented Generation (RAG) is employed to tackle these challenges and has a significant impact on improving LLM performance.<n>We propose a Knowledge Boundary Model (KBM) to express the known/unknown of a given question, and to determine whether a RAG needs to be triggered.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• UNLEARN Efficient Removal of Knowledge in Large Language Models [1.9797215742507548]
  This paper proposes a novel method to achieve this objective called UNLEARN. The approach builds upon subspace methods to identify and specifically target the removal of knowledge without adversely affecting other knowledge in the LLM. Results demonstrate 96% of targeted knowledge can be forgotten while maintaining performance on other knowledge within 2.5% of the original model.
  arXiv  Detail & Related papers  (2024-08-08T00:53:31Z)
• Towards Safer Large Language Models through Machine Unlearning [19.698620794387338]
  Selective Knowledge Unlearning ( SKU) is designed to eliminate harmful knowledge while preserving utility on normal prompts. First stage aims to identify and acquire harmful knowledge within the model, whereas the second is dedicated to remove this knowledge. Our experiments demonstrate that SKU identifies a good balance point between removing harmful information and preserving utility.
  arXiv  Detail & Related papers  (2024-02-15T16:28:34Z)
• Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
  We explore machine unlearning in the domain of large language models (LLMs)<n>This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
  arXiv  Detail & Related papers  (2024-02-13T20:51:58Z)

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.