reviews: 
Comments: This paper addresses the challenge of correcting and updating the knowledge of large language models (LLMs) that have been pre-trained on extensive text data. It introduces a novel approach called the MAssive Language Model Editing Network (MALMEN). MALMEN formulates the parameter shift aggregation as a least square problem, seeking the most effective parameter shifts for all facts to be injected. This approach improves the statistical significance of the editing process, mitigating the issues of gradient accumulation and the cancellation effect. Furthermore, this paper separates the computation between the hyper-network and the language model, enabling the use of arbitrary batch sizes for both neural networks. Exceptional performance on multiple knowledge-intensive tasks is a testament to MALMEN's effectiveness.
Strengths: 1.This is overall a well-written paper: it tackles a very important problem, formulating the parameter shift aggregation as the least square problem. This approach differs from traditional fine-tuning and overcomes the challenges of scalability and memory usage in existing hyper-network methods.

2.This paper focuses on scalability. MALMEN is designed to edit multiple facts simultaneously, making it more practical for mass editing in real-world scenarios. This is a crucial aspect given the need to update the knowledge of large language models comprehensively.

3.Despite being somewhat math-heavy, the paper is written in a very clear and didactic way. I found it easy to follow and an enjoyable read overall.

4.Comprehensive (although not entirely convincing, see below) experiments on various knowledge-intensive NLP tasks and across different LLM architectures. This demonstrates the effectiveness and versatility of the proposed method.
Weaknesses: 1.Baselines are limited. Why not compare with T-Patcher (Huang et al., 2023), which I believe is more suitable for sequential knowledge editing?

2.The criteria for successful edits are, in my opinion, insufficient, in that they do not consider the portability of the edit. Previous work such as Yao et al., 2023, introducing an additional assessment metric, portability, finding that the model-editing methods lack robustness when applied to related one-hop fact or synonyms. 

3.Which layers to apply MALMEN? All layers or some picked layers? Section 5.2 claims that “Edit first FC in FFN” achieves inferior performance. How to select the layer in practical application?

4.The experiments are lacking in qualitative examples, it would be helpful to analyze some success and failure cases to see where the proposed method begins to fail (e.g., with respect to generalization).

5.MALMEN is essentially a combination of meta-learning based (e.g. MEND, Mitchell 2022) and parametric (e.g. ROME, Meng 2022 or MEMIT, Meng 2023) editing ideas that shows some promise. The method is not particularly technically novel (minor point).
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good