Many Natural Language Processing (NLP) tasks, including information extraction, dialogue understanding, and sentiment analysis, can be formulated as structured prediction over sequences or spans. Although Large Language Model (LLM)-based methods have achieved strong performance on standard benchmarks for such tasks, their robustness under natural sentence-level perturbations and their controllability in producing valid structures remain insufficiently studied. In this paper, we propose Locality-aware Constrained Decoding (LoCoDec), a method for LLM-based structured prediction over sequences and spans that formulates decoding as constrained token paraphrasing. LoCoDec combines neighborhood context, which reduces long-text noise, with structural decoding constraints that enforce source-faithful and well-formed outputs. Experiments on sixteen benchmarks of six tasks show that LoCoDec remains competitive in S-F1 and P-F1, while achieving the best R-F1 on all datasets. Ablation results show that removing neighborhood context reduces R-F1 by up to 16.23 points. Overall, LoCoDec improves the robustness and structural consistency of LLM-based structured prediction.

This paper focuses on the task of generating concept sememe trees to study whether Large Language Models (LLMs) can understand and generate domain conceptual knowledge. Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships.To this end, we introduce the Neighbor Semantic Structure (NSS) and Chain-of-Thought (CoT) prompting method to evaluate the effectiveness of various LLMs in generating accurate and comprehensive sememe trees across different domains. The NSS, guided by conceptual metaphors, identifies terms that exhibit significant external systematicity within a hierarchical relational network and incorporates them as examples in the learning process of LLMs. Meanwhile, the CoT prompting method guides LLMs through a systematic analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees.We conduct experiments using datasets drawn from four authoritative terminology manuals and evaluate different LLMs. The experimental results indicate that LLMs possess the capability to capture and represent the conceptual knowledge aspects of domain-specific terms. Moreover, the integration of NSS examples with a structured CoT process allows LLMs to explore domain conceptual knowledge more profoundly, leading to the generation of highly accurate concept sememe trees.