LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model

• URL: http://arxiv.org/abs/2304.06248v1
• Date: Thu, 13 Apr 2023 04:01:14 GMT
• Title: LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
• Authors: Hao Fei, Shengqiong Wu, Jingye Li, Bobo Li, Fei Li, Libo Qin, Meishan Zhang, Min Zhang, Tat-Seng Chua
• Abstract summary: Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential. We propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system.
• Score: 96.889634747943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Universally modeling all typical information extraction tasks (UIE) with one generative language model (GLM) has revealed great potential by the latest study, where various IE predictions are unified into a linearized hierarchical expression under a GLM. Syntactic structure information, a type of effective feature which has been extensively utilized in IE community, should also be beneficial to UIE. In this work, we propose a novel structure-aware GLM, fully unleashing the power of syntactic knowledge for UIE. A heterogeneous structure inductor is explored to unsupervisedly induce rich heterogeneous structural representations by post-training an existing GLM. In particular, a structural broadcaster is devised to compact various latent trees into explicit high-order forests, helping to guide a better generation during decoding. We finally introduce a task-oriented structure fine-tuning mechanism, further adjusting the learned structures to most coincide with the end-task's need. Over 12 IE benchmarks across 7 tasks our system shows significant improvements over the baseline UIE system. Further in-depth analyses show that our GLM learns rich task-adaptive structural bias that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying. Source codes are open at https://github.com/ChocoWu/LasUIE.

Related papers

• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning [8.02547453169677]
  We propose a novel Graph-based Structure-Aware Prompt Learning Model for commonsense reasoning, named G-SAP. In particular, an evidence graph is constructed by integrating multiple knowledge sources, i.e. ConceptNet, Wikipedia, and Cambridge Dictionary. The results reveal a significant advancement over the existing models, especially, with 6.12% improvement over the SoTA LM+GNNs model on the OpenbookQA dataset.
  arXiv  Detail & Related papers  (2024-05-09T08:28:12Z)
• Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Network [29.149367323751413]
  We propose ReStruct, a meta-structure search framework that integrates reasoning into the evolutionary procedure. We show that ReStruct achieves state-of-the-art performance in both recommendation and node classification tasks.
  arXiv  Detail & Related papers  (2024-02-18T09:21:12Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• PIVOINE: Instruction Tuning for Open-world Information Extraction [53.98073623222221]
  We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. We develop a large language model (LLM) that is able to perform Open-world IE to extract desirable entity profiles characterized by (possibly fine-grained) natural language instructions. In particular, we construct INSTRUCTOPENWIKI, a substantial instruction tuning dataset for Open-world IE enriched with a comprehensive corpus, extensive annotations, and diverse instructions.
  arXiv  Detail & Related papers  (2023-05-24T08:52:08Z)
• SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization [67.28453445927825]
  Graph Neural Networks (GNNs) are de facto solutions to structural data learning. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This paper proposes a general GSL framework, SE-GSL, through structural entropy and the graph hierarchy abstracted in the encoding tree.
  arXiv  Detail & Related papers  (2023-03-17T05:20:24Z)
• Unified Structure Generation for Universal Information Extraction [58.89057387608414]
  UIE can universally model different IE tasks, adaptively generate targeted structures, and collaboratively learn general IE abilities from different knowledge sources. Experiments show that UIE achieved the state-of-the-art performance on 4 IE tasks, 13 datasets, and on all supervised, low-resource, and few-shot settings.
  arXiv  Detail & Related papers  (2022-03-23T08:49:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.