Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). In this paper, we identify and investigate the phenomenon of Editing Overfit, where edited models assign disproportionately high probabilities to the edit target, hindering the generalization of new knowledge in complex scenarios. We attribute this issue to the current editing paradigm, which places excessive emphasis on the direct correspondence between the input prompt and the edit target for each edit sample. To further explore this issue, we introduce a new benchmark, EVOKE (EValuation of Editing Overfit in Knowledge Editing), along with finegrained evaluation metrics. Through comprehensive experiments and analysis, we demonstrate that Editing Overfit is prevalent in current editing methods and that common overfitting mitigation strategies are of limited effectiveness in knowledge editing. To overcome this, inspired by LLMs' knowledge recall mechanisms, we propose a new plug-and-play strategy called Learn to Inference (LTI), which introduce a Multi-stage Inference Constraint module to guide the edited models in recalling new knowledge similarly to how unedited LLMs leverage knowledge through in-context learning. Large Language Models (LLMs) have achieved remarkable success across various Natural Language Processing (NLP) tasks (Zhao et al., 2023), yet they often contain outdated or incorrect information, raising concerns about their reliability and factual accuracy. Knowledge Editing (Yao et al., 2023) has emerged as a promising solution to precisely update or correct a model's knowledge. Approaches to knowledge editing fall into two main categories: parameter-preserving methods, such as SERAC (Mitchell et al., 2022) and T-patcher (Huang et al.), which adjust outputs by storing external knowledge, and parameter-modifying methods, which directly alter the model's internal parameters. The latter includes fine-tuning-based methods like FT-L (Zhu et al., 2020), meta-learning approaches such as KE (De Cao et al., 2021) and MEND (Mitchell et al., 2021), and locate-then-edit methods like ROME (Meng et al., 2022a) and MEMIT (Meng et al., 2022b). Although existing methods have achieved promising results, their performance experiences a catastrophic decline when transferred to complex tasks involving reasoning (Yao et al., 2023). For instance, in the representative multi-hop reasoning task, after the LLM is updated with Steve Jobs as the founder of Microsoft, it can easily respond to straightforward questions like "Who is the founder of Microsoft?" with "Steve Jobs."

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration. We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods. For instance, in the challenging task of logical fallacy detection, Evoke scores above 80, while all other baseline methods struggle to reach 20.