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Large Language Models for Generative Information Extraction: A Survey
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Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related works and resources on GitHub (\href{https://github.com/quqxui/Awesome-LLM4IE-Papers}{LLM4IE repository})
Forward citations
Cited by 7 Pith papers
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LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction
Label-aware diagnostic reflection plus two-stage outcome GRPO improves same-backbone IE F1 over SFT, with larger gains under relation-extraction domain shift.
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Syntactic similarity retrieval of demonstrations improves LLM-based automatic term extraction in cross-domain settings, but gains are modest and in-domain lexical retrieval is often competitive or better.
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Using multiple programming languages as code-style prompts during fine-tuning improves LLM information extraction accuracy over single-language prompting.
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An LLM-driven, near-real-time dashboard labels US news articles by topic, lean, and tone, and user studies suggest it helps experts and consumers explore selection and framing bias.
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(Towards) Scalable Reliable Automated Evaluation with Large Language Models
Multi-LLM pairwise Elo ranking with adjustable consensus thresholds produces rankings of competency profiles that average Spearman ρ≈0.83 with expert judgments.
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Effective and Efficient Schema-aware Information Extraction Using On-Device Large Language Models
DLISC, a dual-LoRA two-stage schema-aware extraction method with incremental schema caching, reports better F1 and lower latency than three RAG baselines on two IE datasets, though the comparison lacks error bars and code.
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