Supervised fine-tuning (SFT) is widely used to align large language models (LLMs) with information extraction (IE) tasks, such as named entity recognition (NER).However, annotating such fine-grained labels and training domainspecific models is costly.Existing works typically train a unified model across multiple domains, but such approaches lack adaptation and scalability since not all training data benefits target domains and scaling trained models remains challenging.We propose the SaM framework, which dynamically Selects and Merges expert models at inference time.Specifically, for a target domain, we select domain-specific experts pre-trained on existing domains based on (i) domain similarity to the target domain and (ii) performance on sampled instances, respectively.The experts are then merged to create task-specific models optimized for the target domain.By dynamically merging experts beneficial to target domains, we improve generalization across various domains without extra training.Additionally, experts can be added or removed conveniently, leading to great scalability.Extensive experiments on multiple benchmarks demonstrate our framework's effectiveness, which outperforms the unified model by an average of 10%.We further provide insights into potential improvements, practical experience, and extensions of our framework.

Large language models (LLMs) possess extensive prior knowledge and powerful in-context learning (ICL) capabilities, presenting significant opportunities for low-resource tasks. Though effective, several key issues still have not been well-addressed when focusing on zero-shot named entity recognition (NER), including the misalignment between model and human definitions of entity types, and confusion of similar types. This paper proposes an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. Specifically, we leverage the model to summarize the definition of each entity type and the distinctions between similar types (i.e., entity type guidelines). An iterative process is introduced to continually adjust and improve these guidelines. Additionally, since high-quality demonstrations are crucial for effective learning yet challenging to obtain in zero-shot scenarios, we design a strategy motivated by self-consistency and prototype learning to extract reliable and diverse pseudo samples from the model’s predictions. Experiments on four benchmarks demonstrate the effectiveness of our framework, showing consistent performance improvements.