Named Entity Recognition (NER) and Relation Extraction (RE) are two fundamental and interdependent tasks in information extraction (IE), aiming to identify entities and relations from unstructured text. Recently, generative methods have become mainstream instead of discriminative methods for IE, especially joint multi-task IE, due to their promising performance and flexibility. For joint NER and RE, existing methods suffer from misalignment between entities and relations, as well as misalignment among relations. To address these issues, we propose AnchorAlign, a novel generative method enhanced by anchor alignment. Specifically, we first introduce an anchor entity selection mechanism to identify key entities in the text as anchor points, which serve as semantic pivots to bridge the two tasks. Then, we design a dual-level anchor alignment module: at the semantic level, we construct a cross-task semantic alignment space to align the semantic representations of anchor entities and their associated relations; at the generation level, we introduce an anchor-guided generation constraint to guide the model to generate entities and relations with strict alignment based on the anchor points. Extensive experiments on five benchmark datasets show that AnchorAlign outperforms state-of-the-art baselines, demonstrating its effectiveness. Our work provides a new perspective for optimizing the joint modeling of NER and RE, and has potential to be extended to more complex multi-task IE such as NER and Event Extraction (EE).

Retrieval-Augmented Generation (RAG) has been widely adopted to enhance large language models (LLMs) by incorporating external knowledge. However, the two main existing paradigms struggle with multi-hop reasoning: aggregate-first approaches suffer from high construction costs and limited adaptability to dynamic knowledge, while dynamic-first approaches rely heavily on LLM reasoning and are prone to error propagation across reasoning steps. To address these limitations, we propose SR-RAG, a symbolic reasoning framework for multi-hop question answering. SR-RAG integrates the advantages of both paradigms by dynamically generating sub-questions, performing information retrieval and symbolic encoding based on an on-the-fly graph, and using a symbolic verifier to formally validate intermediate reasoning steps to ensure the correctness of intermediate answers and the completeness of the reasoning chain . We evaluate SR-RAG on multiple multi-hop benchmarks and a medical dataset. Experimental results demonstrate that it significantly improves both accuracy and robustness.