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Massive Editing for Large Language Models via Meta Learning

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arxiv 2311.04661 v3 pith:PI4J4F2L submitted 2023-11-08 cs.CL cs.LG

Massive Editing for Large Language Models via Meta Learning

Chenmien Tan , Ge Zhang , Jie Fu This is my paper
classification cs.CL cs.LG
keywords editinglanguagefactshyper-networkknowledgemalmenlargelearning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which necessitates rectifying the knowledge of the language model (LM) after the training. A promising approach involves employing a hyper-network to generate parameter shift, whereas existing hyper-networks suffer from inferior scalability in synchronous editing operation amount. To mitigate the problem, we propose the MAssive Language Model Editing Network (MALMEN), which formulates the parameter shift aggregation as the least square problem, subsequently updating the LM parameters using the normal equation. To accommodate editing multiple facts simultaneously with limited memory budgets, we separate the computation on the hyper-network and LM, enabling arbitrary batch size on both neural networks. Our method is evaluated by editing up to thousands of facts on LMs with different architectures, i.e., BERT-base, GPT-2, T5-XL (2.8B), and GPT-J (6B), across various knowledge-intensive NLP tasks, i.e., closed book fact-checking and question answering. Remarkably, MALMEN is capable of editing hundreds of times more facts than strong baselines with the identical hyper-network architecture and outperforms editor specifically designed for GPT. Our code is available at https://github.com/ChenmienTan/malmen.

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Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing

    cs.LG 2026-06 unverdicted novelty 7.0

    RRDA introduces a router plus separate edit and locality adapters for memory-assisted knowledge editing, reporting highest accuracy on CounterFact, ZsRE, and MQuAKE-CF across two 8B models.

  2. Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment

    cs.AI 2026-05 unverdicted novelty 7.0

    Introduces Latent Adversarial Robustification and Rank-Constrained Subspace Learning to enable robust generalization in multimodal knowledge editing through adversarial subspace alignment.

  3. ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models

    cs.CL 2026-07 conditional novelty 6.0

    ForgetBench is a sequential-editing benchmark showing that existing knowledge-editing methods trade long-term retention against generalization, while structured contexts mask parametric forgetting.

  4. When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing

    cs.LG 2026-06 unverdicted novelty 6.0

    Introduces route-specialized dual adapters that route prompts to either an edit adapter or a locality adapter, achieving highest accuracy on CF, ZSRE, and MQuAKE benchmarks for 7B/8B models.

  5. Don't Use a Cannon to Kill a Fly: Lightweight Model Editing for LLMs to Correct Deprecated API Recommendations

    cs.SE 2025-11 conditional novelty 6.0

    AdaLoRA-L restricts edits to API-specific layers and raises specificity by 33–836% (relative) on a new 3,000+ instance benchmark while staying close to AdaLoRA's effectiveness.

  6. Break Through the Compression Bottleneck: From Theory to Practice

    cs.CL 2026-05 reject novelty 5.0

    The paper asserts a first proof that low-rank decomposition and quantization are non-orthogonal tools for LLM compression, recommends low-rank-first ordering, and adds a diagonal scaling fix (DAM) that reduces the com...

  7. Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression

    cs.AI 2026-04 unverdicted novelty 5.0

    LightEdit enables scalable lifelong knowledge editing in LLMs via selective knowledge retrieval and probability suppression during decoding, outperforming prior methods on ZSRE, Counterfact, and RIPE while reducing tr...