S eq MMR : Sequential Model Merging and LLM Routing for Enhanced Batched Sequential Knowledge Editing (2025)

Abstract: Model knowledge editing enables the efficient correction of erroneous information and the continuous updating of outdated knowledge within language models. While existing research has demonstrated strong performance in single-instance or few-instance sequential editing and one-time massive editing scenarios, the batched sequential editing paradigm remains a significant challenge. The primary issue lies in the model’s tendency to gradually forget previously edited knowledge and become increasingly unstable after multiple iterations of batched editing. To address these challenges, we propose **SeqMMR**, an enhanced framework for batched sequential knowledge editing that leverages **Seq**uential **M**odel **M**erging and a model **R**outer. Our approach iteratively merges parameters from current batch-edited models with those of their predecessors, ensuring that newly emerging knowledge is integrated while mitigating the forgetting of previously edited knowledge. Furthermore, the model router directs queries unrelated to the edited knowledge to an unedited model backup, preventing unintended alterations in model predictions. Extensive experiments across various datasets demonstrate that our approach effectively mitigates knowledge forgetting, improves performance across all previous batches, and better preserves the model’s general capabilities.

## introduction large language models (llms) have exhibited remarkable performance across a wide range of natural language processing (nlp) tasks, serving as repositories of extensive factual knowledge within their parameters @xcite @xcite . however, their knowledge remains inherently limited in coverage and accuracy, often relying on outdated information @xcite @xcite corresponding author @xcite or generating erroneous, hallucinatory, and biased content @xcite @xcite @xcite . given the continuous evolution of world knowledge and the necessity of correcting inaccuracies, the field of knowledge editing has garnered increasing research attention. existing knowledge editing methods can be broadly classified into parameter-updating and parameter-preserving approaches. parameterupdating methods @xcite @xcite follow the locate-then-edit paradigm to modify specific model parameters associated with knowledge storage. in contrast, parameter-preserving methods @xcite @xcite @xcite either train hypernetworks to dynamically adjust model outputs or modify outputs by appending constructed prompts to input queries, leaving the model parameters unchanged. while prior research has primarily focused on single-instance sequential editing or one-time massive editing, real-world model maintenance requires batched and sequential editing to continuously update knowledge as it evolves. icl-based methods @xcite face inefficiencies and temporary edits, while metalearning-based methods @xcite are optimized for individual instances, limiting batch effectiveness. in contrast, parameterupdating approaches @xcite @xcite enable large-scale editing in a single step, making them more practical for continuous model updates. a critical challenge in batched sequential editing is ensuring the stability of sequential parameter updates. recent studies @xcite @xcite have shown that parameter-updating methods in sequential editing tasks suffer from model degradation due to the accumulation of parameter shifts, prompting efforts to mitigate this issue. @xcite ) also observed that models continuously forget previously edited knowledge and loses the ability to perform downstream tasks. addressing these limitations is crucial for developing scalable and stable knowledge editing frameworks capable of supporting continuous, large-scale updates in llms. to this end, we propose an enhanced batched sequential knowledge editing framework based on sequential model merging and a model router. specifically, we iteratively merge the parameters of the current batch-edited model with those of the previous model, enabling the merged model to retain the latest edited knowledge while preventing the forgetting of previously stored knowledge. similar to sequential parameter updates, sequential model merging needs to address interference between merging parameters. unlike multitask model merging, sequential merging does not have access to model parameters from future time steps, necessitating an interference handling solution based on self-awareness at the current time step. furthermore, to better handle knowledge outside the editing scope, we design a model router that routes edit-unrelated queries to the unedited model backup, ensuring their predictions are unaffected by the editing process. the main contributions of our work can be summarized as follows: ## conclusion in this work, we introduces seqmmr, a novel approach to addressing the challenges in batched sequential knowledge editing for large language models. by iteratively merging the current batch-edited model with the previous merged one, our method preserves newly integrated knowledge while mitigating the forgetting of prior edits. additionally, the incorporation of a model router enables editing-unrelated queries to be processed by an unedited model backup, leading to optimal locality on these queries. extensive experiments show that seqmmr effectively alleviates knowledge forgetting and enhances the model's performance across all previous edit batches, while also ensuring stable general capabilities. this framework provides a scalable and stable solution for continuous knowledge updates in large language models.