# Can Knowledge Editing Really Correct Hallucinations?

arXiv: https://arxiv.org/abs/2410.16251 · HTML: https://mlsanity.com/paper/2410.16251/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 104 of its 134 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 30 of 104)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions](https://mlsanity.com/paper/2305.14795/) (2023)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://mlsanity.com/paper/2305.12740/) (2023)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://mlsanity.com/paper/2211.11031/) (2022)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models](https://mlsanity.com/paper/2301.04213/) (2023)
- [Evaluating the Ripple Effects of Knowledge Editing in Language Models](https://mlsanity.com/paper/2307.12976/) (2023)
- [AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models](https://mlsanity.com/paper/2410.02355/) (2024)
- [Modifying Memories in Transformer Models](https://mlsanity.com/paper/2012.00363/) (2020)
- [Knowledge Editing for Large Language Models: A Survey](https://mlsanity.com/paper/2310.16218/) (2023)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [PMET: Precise Model Editing in a Transformer](https://mlsanity.com/paper/2308.08742/) (2023)
- [A Comprehensive Study of Knowledge Editing for Large Language Models](https://mlsanity.com/paper/2401.01286/) (2024)
- [Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://mlsanity.com/paper/2401.04700/) (2024)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [Model Editing at Scale leads to Gradual and Catastrophic Forgetting](https://mlsanity.com/paper/2401.07453/) (2024)
- [Evaluating the Social Impact of Generative AI Systems in Systems and Society](https://mlsanity.com/paper/2306.05949/) (2023)
- [WISE: Rethinking the Knowledge Memory for Lifelong Model Editing of Large Language Models](https://mlsanity.com/paper/2405.14768/) (2024)
- [TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space](https://mlsanity.com/paper/2402.17811/) (2024)
- [Detoxifying Large Language Models via Knowledge Editing](https://mlsanity.com/paper/2403.14472/) (2024)
- [A Primer on the Inner Workings of Transformer-based Language Models](https://mlsanity.com/paper/2405.00208/) (2024)
- [Trustworthiness in Retrieval-Augmented Generation Systems: A Survey](https://mlsanity.com/paper/2409.10102/) (2024)

## Built on by (most cited first, 28 of 28)

- [AnyEdit: Edit Any Knowledge Encoded in Language Models](https://mlsanity.com/paper/2502.05628/) (2025)
- [Context-DPO: Aligning Language Models for Context-Faithfulness](https://mlsanity.com/paper/2412.15280/) (2024)
- [Reinforced Lifelong Editing for Language Models](https://mlsanity.com/paper/2502.05759/) (2025)
- [Steering off Course: Reliability Challenges in Steering Language Models](https://mlsanity.com/paper/2504.04635/) (2025)
- [The Mirage of Model Editing: Revisiting Evaluation in the Wild](https://mlsanity.com/paper/2502.11177/) (2025)
- [Can Multimodal LLMs Perform Time Series Anomaly Detection?](https://mlsanity.com/paper/2502.17812/) (2025)
- [CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners](https://mlsanity.com/paper/2503.16356/) (2025)
- [UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models](https://mlsanity.com/paper/2505.14679/) (2025)
- [COMPKE: Complex Question Answering under Knowledge Editing](https://mlsanity.com/paper/2506.00829/) (2025)
- [Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing](https://mlsanity.com/paper/2505.12636/) (2025)
- [UniErase: Towards Balanced and Precise Unlearning in Language Models](https://mlsanity.com/paper/2505.15674/) (2025)
- [Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing](https://mlsanity.com/paper/2506.03490/) (2025)
- [Provable Benefits of In-Tool Learning for Large Language Models](https://mlsanity.com/paper/2508.20755/) (2025)
- [Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs](https://mlsanity.com/paper/2502.14645/) (2025)
- [Revisiting Hallucination Detection with Effective Rank-based Uncertainty](https://mlsanity.com/paper/2510.08389/) (2025)
- [Probing LLM Hallucination from Within: Perturbation-Driven Approach via Internal Knowledge](https://mlsanity.com/paper/2411.09689/) (2024)
- [Editing Across Languages: A Survey of Multilingual Knowledge Editing](https://mlsanity.com/paper/2505.14393/) (2025)
- [Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm](https://mlsanity.com/paper/2506.20606/) (2025)
- [Retention analysis of edited knowledge after fine-tuning](https://mlsanity.com/paper/2507.14198/) (2025)
- [EtCon: Edit-then-Consolidate for Reliable Knowledge Editing](https://mlsanity.com/paper/2512.04753/) (2025)
- [Towards Effective Model Editing for LLM Personalization](https://mlsanity.com/paper/2512.13676/) (2025)
- [Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning](https://mlsanity.com/paper/2602.19612/) (2026)
- [Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs](https://mlsanity.com/paper/2606.17057/) (2026)
- [Exposing the Illusion of Erasure in Knowledge Editing for LLMs](https://mlsanity.com/paper/2606.23276/) (2026)
- [Aligning Language Models with Real-time Knowledge Editing](https://mlsanity.com/paper/2508.01302/) (2025)
- [Distributed Multi-Layer Editing for Rule-Level Knowledge in Large Language Models](https://mlsanity.com/paper/2604.08284/) (2026)
- [Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)](https://mlsanity.com/paper/2605.26942/) (2026)
- [Can Factual Opinions Be Edited (Manipulated) in Large Language Models?](https://mlsanity.com/paper/2606.03096/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual information in generated content, despite their superior capacities across tasks. Meanwhile, knowledge editing has been developed as a new popular paradigm to correct erroneous factual knowledge encoded in LLMs with the advantage of avoiding retraining from scratch. However, a common issue of existing evaluation datasets for knowledge editing is that they do not ensure that LLMs actually generate hallucinated answers to the evaluation questions before editing. When LLMs are evaluated on such datasets after being edited by different techniques, it is hard to directly adopt the performance to assess the effectiveness of different knowledge editing methods in correcting hallucinations. Thus, the fundamental question remains insufficiently validated: Can knowledge editing really correct hallucinations in LLMs? We proposed HalluEditBench to holistically benchmark knowledge editing methods in correcting real-world hallucinations. First, we rigorously construct a massive hallucination dataset with 9 domains, 26 topics and more than 6,000 hallucinations. Then, we assess the performance of knowledge editing methods in a holistic way on five dimensions including Efficacy, Generalization, Portability, Locality, and Robustness. Through HalluEditBench, we have provided new insights into the potentials and limitations of different knowledge editing methods in correcting hallucinations, which could inspire future improvements and facilitate progress in the field of knowledge editing.