# WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs

arXiv: https://arxiv.org/abs/2503.05683 · HTML: https://mlsanity.com/paper/2503.05683/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 85 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 30 of 66)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021)
- [GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection](https://mlsanity.com/paper/2403.03507/) (2024)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://mlsanity.com/paper/2310.03214/) (2023)
- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://mlsanity.com/paper/2405.14831/) (2024)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://mlsanity.com/paper/2211.11031/) (2022)
- [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021)
- [Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities](https://mlsanity.com/paper/2408.07666/) (2024)
- [Patching open-vocabulary models by interpolating weights](https://mlsanity.com/paper/2208.05592/) (2022)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)

## Built on by (most cited first, 26 of 26)

- [UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models](https://mlsanity.com/paper/2505.14679/) (2025)
- [Fine-tuning Done Right in Model Editing](https://mlsanity.com/paper/2509.22072/) (2025)
- [Edit Less, Achieve More: Dynamic Sparse Neuron Masking for Lifelong Knowledge Editing in LLMs](https://mlsanity.com/paper/2510.22139/) (2025)
- [CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing](https://mlsanity.com/paper/2602.15823/) (2026)
- [RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World](https://mlsanity.com/paper/2604.05096/) (2026)
- [Memorization and Knowledge Injection in Gated LLMs](https://mlsanity.com/paper/2504.21239/) (2025)
- [Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs](https://mlsanity.com/paper/2511.20892/) (2025)
- [Higher-Order Knowledge Representations for Agentic Scientific Reasoning](https://mlsanity.com/paper/2601.04878/) (2026)
- [Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models](https://mlsanity.com/paper/2607.19604/) (2026)
- [RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories](https://mlsanity.com/paper/2512.04144/) (2025)
- [Norm Anchors Make Model Edits Last](https://mlsanity.com/paper/2602.02543/) (2026)
- [Reversible Lifelong Model Editing via Semantic Routing-Based LoRA](https://mlsanity.com/paper/2603.11239/) (2026)
- [Modality-Decoupled Online Recursive Editing](https://mlsanity.com/paper/2605.20273/) (2026)
- [Aligning Language Models with Real-time Knowledge Editing](https://mlsanity.com/paper/2508.01302/) (2025)
- [Conflict-Resolving and Sharpness-Aware Minimization for Generalized Knowledge Editing with Multiple Updates](https://mlsanity.com/paper/2602.03696/) (2026)
- [HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing](https://mlsanity.com/paper/2605.08143/) (2026)
- [Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing](https://mlsanity.com/paper/2607.01978/) (2026)
- [Can a Language Model Learn Facts Continually in Its Weights?](https://mlsanity.com/paper/2607.11020/) (2026)
- [KLOD: Locality-Preserving Knowledge Editing via Non-Target Distribution Preservation](https://mlsanity.com/paper/2608.27839/) (2026)
- [Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs](https://mlsanity.com/paper/2609.00184/) (2026)
- [Popular Knowledge Propagates More Errors in LLM Knowledge Updating](https://mlsanity.com/paper/2609.08067/) (2026)
- [The evolution of sex for artificial intelligence: a population-genetic framework for multigenerational model populations](https://mlsanity.com/paper/2609.18560/) (2026)
- [Generalizable Lifelong Model Editing via Preference Optimization](https://mlsanity.com/paper/2609.36748/) (2026)
- [MedKIT: Evaluating Knowledge Integration and Generalization in Large Language Models](https://mlsanity.com/paper/2609.38543/) (2026)
- [PAPER2LLM++: Continual Self-Evolution of LLMs from Research Papers](https://mlsanity.com/paper/2610.02793/) (2026)
- [LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs](https://mlsanity.com/paper/2610.11160/) (2026)

## Abstract (arXiv)

Keeping large language models factually up-to-date is crucial for deployment, yet costly retraining remains a challenge. Knowledge editing offers a promising alternative, but methods are only tested on small-scale or synthetic edit benchmarks. In this work, we aim to bridge research into lifelong knowledge editing to real-world edits at a practically relevant scale. We first introduce WikiBigEdit; a large-scale benchmark of real-world Wikidata edits, built to automatically extend lifelong for future-proof benchmarking. In its first instance, it includes over 500K question-answer pairs for knowledge editing alongside a comprehensive evaluation pipeline. Finally, we use WikiBigEdit to study existing knowledge editing techniques' ability to incorporate large volumes of real-world facts and contrast their capabilities to generic modification techniques such as retrieval augmentation and continual finetuning to acquire a complete picture of the practical extent of current lifelong knowledge editing.