Lifelong model editing aims to dynamically adjust a model’s output with respect to specific facts, knowledge points, or behaviors, enabling the model to adapt to the ever-changing demands of the real world without requiring retraining. While some retrieval-based methods have demonstrated potential in lifelong editing scenarios by storing edited knowledge in external memory, they often suffer from limitations in usability, such as requiring additional training corpora or lacking support for reversible and detachable edits.To address these issues, we propose a plug-and-play method for knowledge retrieval and storage, i.e., Layer-Level Prompting (LLP), which enables seamless and efficient lifelong model editing. In our LLP framework, the reasoning process of LLMs is divided into two stages, respectively knowledge retrieval (Think) and knowledge injection(Recall). Specifically, the knowledge retrieval process is performed in the early layers of the model. Based on the retrieved information, the model is guided to access the updated knowledge stored in the subsequent layer to complete the knowledge editing process. Experimental results demonstrate that our method consistently outperforms existing techniques on lifelong model editing tasks, achieving superior performance on question answering and hallucination benchmarks across different LLMs.

Multi-hop question answering is a challenging task that requires capturing information from different positions in multiple documents. Recently, several methods propose to enhance Large Language Models (LLMs) by incorporating structured knowledge, aiming to grasp key information for solving this task. Despite certain achievements, they still face the following challenges: 1) The neglect of text-based reasoning capabilities. 2) Information redundancy between text and triples. 3) Information loss during structured knowledge extraction. To solve the above challenges, in this paper, we propose Dynamic Combination of Structured Knowledge (DCSK), a novel framework for integrating text-based and triple-based paradigms. Following Occam’s Razor, DCSK dynamically determine the necessity of structured knowledge by the designed multi-faceted evaluation, which systematically assess the correctness, clarity, and informativeness of text-based prediction. For questions that require structured knowledge, we develop an iterative fact refiner that screens for question-relevant triples, verifies their factual adequacy, and thereby effectively excludes irrelevant and redundant information. Furthermore, based on the verification, we construct an adaptive knowledge reasoner that dynamically adjusts the need for text supplementation, thus mitigating the information deficiency in selected triples. Extensive experiments on three MHQA datasets demonstrate the efficiency and effectiveness of DCSK.

Recently, few-shot Named Entity Recognition (NER) has attracted significant attention due to the high cost of obtaining high-quality labeled data. Decomposition-based methods have demonstrated remarkable performance on this task, which initially train a type-independent span detector and subsequently classify the detected spans based on their types. However, this framework has an evident drawback as a domain-agnostic detector cannot ensure the identification of only those entity spans that are specific to the target domain. To address this issue, we propose Double-Checker, which leverages collaboration between Large Language Models (LLMs) and small models. Specifically, we employ LLMs to verify candidate spans predicted by the small model and eliminate any spans that fall outside the scope of the target domain. Extensive experiments validate the effectiveness of our method, consistently yielding improvements over two baseline approaches. Our code is available at https://github.com/fanshu6hao/Double-Checker.