# Structure-Aware Decoding Mechanisms for Complex Entity Extraction with Large-Scale Language Models

arXiv: https://arxiv.org/abs/2512.13980 · HTML: https://mlsanity.com/paper/2512.13980/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 12 of 12)

- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [FiNER: Financial Numeric Entity Recognition for XBRL Tagging](https://mlsanity.com/paper/2203.06482/) (2022)
- [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021)
- [Structure-Learnable Adapter Fine-Tuning for Parameter-Efficient Large Language Models](https://mlsanity.com/paper/2509.03057/) (2025)
- [An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition](https://mlsanity.com/paper/2208.04534/) (2022)
- [MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation](https://mlsanity.com/paper/2210.07443/) (2022)
- [Deep Learning Approach for Clinical Risk Identification Using Transformer Modeling of Heterogeneous EHR Data](https://mlsanity.com/paper/2511.04158/) (2025)
- [Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition](https://mlsanity.com/paper/2305.07266/) (2023)
- [In-Context Learning for Few-Shot Nested Named Entity Recognition](https://mlsanity.com/paper/2402.01182/) (2024)
- [Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Models](https://mlsanity.com/paper/2510.10613/) (2025)
- [Contrastive Knowledge Transfer and Robust Optimization for Secure Alignment of Large Language Models](https://mlsanity.com/paper/2510.27077/) (2025)
- [Multi-Scale Feature Fusion and Graph Neural Network Integration for Text Classification with Large Language Models](https://mlsanity.com/paper/2511.05752/) (2025)

## Built on by (most cited first, 3 of 3)

- [CaliCausalRank: Calibrated Multi-Objective Ad Ranking with Robust Counterfactual Utility Optimization](https://mlsanity.com/paper/2602.18786/) (2026)
- [Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space](https://mlsanity.com/paper/2602.23753/) (2026)
- [Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models](https://mlsanity.com/paper/2603.04647/) (2026)

## Abstract (arXiv)

This paper proposes a structure-aware decoding method based on large language models to address the difficulty of traditional approaches in maintaining both semantic integrity and structural consistency in nested and overlapping entity extraction tasks. The method introduces a candidate span generation mechanism and structured attention modeling to achieve unified modeling of entity boundaries, hierarchical relationships, and cross-dependencies. The model first uses a pretrained language model to obtain context-aware semantic representations, then captures multi-granular entity span features through candidate representation combinations, and introduces hierarchical structural constraints during decoding to ensure consistency between semantics and structure. To enhance stability in complex scenarios, the model jointly optimizes classification loss and structural consistency loss, maintaining high recognition accuracy under multi-entity co-occurrence and long-sentence dependency conditions. Experiments conducted on the ACE 2005 dataset demonstrate significant improvements in Accuracy, Precision, Recall, and F1-Score, particularly in nested and overlapping entity recognition, where the model shows stronger boundary localization and structural modeling capability. This study verifies the effectiveness of structure-aware decoding in complex semantic extraction tasks, provides a new perspective for developing language models with hierarchical understanding, and establishes a methodological foundation for high-precision information extraction.