# YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction

arXiv: https://arxiv.org/abs/2312.15548 · HTML: https://mlsanity.com/paper/2312.15548/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 24 Dec 2023.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning](https://mlsanity.com/paper/2012.15022/) (2020)
- [Universal Information Extraction as Unified Semantic Matching](https://mlsanity.com/paper/2301.03282/) (2023)
- [ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning](https://mlsanity.com/paper/2104.04697/) (2021)
- [CLEVE: Contrastive Pre-training for Event Extraction](https://mlsanity.com/paper/2105.14485/) (2021)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective](https://mlsanity.com/paper/2204.04391/) (2022)
- [CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction](https://mlsanity.com/paper/2204.03871/) (2022)
- [IPRE: a Dataset for Inter-Personal Relationship Extraction](https://mlsanity.com/paper/1907.12801/) (2019)
- [Zero-shot Triplet Extraction by Template Infilling](https://mlsanity.com/paper/2212.10708/) (2022)

## Built on by (most cited first, 19 of 19)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [AutoRE: Document-Level Relation Extraction with Large Language Models](https://mlsanity.com/paper/2403.14888/) (2024)
- [Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.08690/) (2025)
- [MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction](https://mlsanity.com/paper/2509.09082/) (2025)
- [IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus](https://mlsanity.com/paper/2402.14710/) (2024)
- [Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://mlsanity.com/paper/2406.11192/) (2024)
- [CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph](https://mlsanity.com/paper/2406.17231/) (2024)
- [RUIE: Retrieval-based Unified Information Extraction using Large Language Model](https://mlsanity.com/paper/2409.11673/) (2024)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)
- [Schema as Parameterized Tools for Universal Information Extraction](https://mlsanity.com/paper/2506.01276/) (2025)
- [Long-Document QA with Chain-of-Structured-Thought and Fine-Tuned SLMs](https://mlsanity.com/paper/2603.29232/) (2026)
- [MAQInstruct: Instruction-based Unified Event Relation Extraction](https://mlsanity.com/paper/2502.03954/) (2025)
- [Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task](https://mlsanity.com/paper/2402.14494/) (2024)
- [A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction](https://mlsanity.com/paper/2407.10953/) (2024)
- [Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis](https://mlsanity.com/paper/2502.03843/) (2025)
- [Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution](https://mlsanity.com/paper/2503.03201/) (2025)
- [ToMMeR -- Efficient Entity Mention Detection from Large Language Models](https://mlsanity.com/paper/2510.19410/) (2025)
- [SCIR: A Self-Correcting Iterative Refinement Framework for Enhanced Information Extraction Based on Schema](https://mlsanity.com/paper/2512.12337/) (2025)
- [ProUIE: A Macro-to-Micro Progressive Learning Method for LLM-based Universal Information Extraction](https://mlsanity.com/paper/2604.10633/) (2026)

## Abstract (arXiv)

The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures. Recent work has proposed methods based on large language models to uniformly model different information extraction tasks. However, these existing methods are deficient in their information extraction capabilities for Chinese languages other than English. In this paper, we propose an end-to-end chat-enhanced instruction tuning framework for universal information extraction (YAYI-UIE), which supports both Chinese and English. Specifically, we utilize dialogue data and information extraction data to enhance the information extraction performance jointly. Experimental results show that our proposed framework achieves state-of-the-art performance on Chinese datasets while also achieving comparable performance on English datasets under both supervised settings and zero-shot settings.