YAYI-UIE: A Comprehensive Chat-Enhanced Framework for Universal Information Extraction in Chinese and English

Core Concepts

YAYI-UIE is an end-to-end chat-enhanced instruction tuning framework that supports both Chinese and English for universal information extraction, leveraging dialogue data and comprehensive Chinese and English information extraction datasets to enhance the model's performance.

Abstract

The paper proposes YAYI-UIE, an end-to-end chat-enhanced instruction tuning framework for universal information extraction that supports both Chinese and English. The framework consists of two main steps: Instruction Tuning for Chat: The authors utilize dialogue data to fine-tune a base language model, obtaining a chat model with enhanced instruction-following abilities and common understanding of open-world languages. Instruction Tuning for Information Extraction: The authors construct the most comprehensive Chinese instruction tuning benchmark for universal information extraction, combining it with existing English datasets. The chat model is further fine-tuned on this combined dataset to obtain the universal information extraction model, which can generate structured outputs for various information extraction tasks, including named entity recognition, relation extraction, and event extraction. The experimental results demonstrate that YAYI-UIE achieves state-of-the-art performance on Chinese datasets while also maintaining strong performance on English datasets, under both supervised and zero-shot settings.

Stats

The proposed framework supports both Chinese and English information extraction tasks. The authors construct the largest and most comprehensive Chinese instruction tuning benchmark for universal information extraction, covering 16 datasets across various domains. The combined dataset used for instruction tuning includes both Chinese and English information extraction datasets.

Quotes

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Key Insights Distilled From

YAYI-UIE

by Xinglin Xiao... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2312.15548.pdf

Deeper Inquiries

How can the chat-enhanced instruction tuning framework be extended to support other languages beyond Chinese and English?

To extend the chat-enhanced instruction tuning framework to support languages beyond Chinese and English, several steps can be taken: Data Collection: Gather dialogue data in the target language to fine-tune the base LLM for chat understanding. This data should cover a wide range of topics and domains to ensure the model's generalization capabilities. Instruction Tuning: Develop a comprehensive instruction tuning benchmark dataset in the new language for information extraction tasks. This dataset should include diverse tasks, label schemas, and domains to train the model effectively. Model Training: Utilize the dialogue data and the new language instruction benchmark to fine-tune the LLM for chat understanding and information extraction in the target language. This step will help the model adapt to the linguistic nuances and structures of the new language. Evaluation and Iteration: Evaluate the model's performance on a variety of tasks and datasets in the new language. Iterate on the training process by incorporating feedback and making adjustments to improve the model's accuracy and generalization. By following these steps and customizing the framework for the specific linguistic characteristics of the target language, the chat-enhanced instruction tuning framework can be extended to support a wide range of languages beyond Chinese and English.

What are the potential limitations of the current approach, and how can it be further improved to handle more complex or ambiguous information extraction scenarios?

Limitations: Data Bias: The current approach may suffer from data bias if the training data is not diverse enough, leading to performance issues on unseen or complex datasets. Language Specificity: The model's performance may vary across languages due to the linguistic differences and nuances present in each language. Task Complexity: Handling complex or ambiguous information extraction scenarios may challenge the model's ability to generate accurate outputs. Improvements: Data Augmentation: Incorporate data augmentation techniques to increase the diversity of training data and reduce bias. Multilingual Training: Implement multilingual training to enhance the model's ability to handle various languages and improve cross-lingual generalization. Fine-tuning Strategies: Explore advanced fine-tuning strategies, such as curriculum learning or reinforcement learning, to improve the model's performance on complex tasks. Ensemble Models: Utilize ensemble models to combine the strengths of multiple models and enhance performance on challenging scenarios. Continuous Learning: Implement mechanisms for continuous learning to adapt the model to evolving data and improve its performance over time. By addressing these limitations and implementing these improvements, the framework can better handle complex and ambiguous information extraction scenarios.

What other types of auxiliary data or pretraining techniques could be leveraged to further enhance the model's performance and generalization capabilities for information extraction tasks?

Knowledge Graphs: Incorporate knowledge graphs to provide additional context and background information for the model to improve entity and relation extraction accuracy. Domain-Specific Data: Include domain-specific data to fine-tune the model for specialized tasks and improve performance on specific domains. Cross-Domain Pretraining: Pretrain the model on a diverse range of domains to enhance its ability to generalize across different types of information extraction tasks. Multi-Task Learning: Implement multi-task learning to train the model on multiple related tasks simultaneously, improving its overall performance and generalization capabilities. Weakly Supervised Learning: Utilize weakly supervised learning techniques to train the model with limited labeled data, enabling it to extract information from unstructured text more effectively. Adversarial Training: Employ adversarial training to enhance the model's robustness and improve its performance on challenging or adversarial datasets. Active Learning: Implement active learning strategies to select the most informative data samples for model training, optimizing the learning process and enhancing performance.