KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction

Core Concepts

知識を構造化し、LLMにコーディングするためのKnowCoderスキーマ表現方法

Abstract

KnowCoderは、大規模言語モデル（LLM）を使用してUniversal Information Extraction（UIE）を実行するためのコード生成を目的としています。
KnowCoderは、Pythonクラスに異なるスキーマを一貫して変換するコードスタイルのスキーマ表現方法を提案します。
2段階の学習フレームワークを備えた効果的な学習フレームワークであることが特徴です。
Abstract:

KnowCoderは、Pythonクラスに異なるスキーマを一貫して変換するコードスタイルのスキーマ表現方法を提案します。
大規模なコード形式のスキーマライブラリが構築されました。
2段階の学習フレームワークにより、LLMが異なるIEタスクで強力な汎化能力を示すことが可能です。
Introduction:

情報抽出（IE）は明示的かつ構造化された知識を抽出することを目指します。
UIEタスクでは、さまざまな知識を単一モデルで抽出することが提案されています。
Data Extraction:

"1.5B個の自動生成データでコード事前トレーニング後、KnowCoderはF1ポイントでLLaMA2に比べて49.8%相対向上しました。"
"指示チューニング後、KnowCoderは未知のスキーマでも強力な汎化能力を発揮し、ゼロショット設定および低リソース設定ではそれぞれ最大12.5%および21.9%向上しました。"

Stats

"1.5B個の自動生成データでコード事前トレーニング後、KnowCoderはF1ポイントでLLaMA2に比べて49.8%相対向上しました。"
"指示チューニング後、KnowCoderは未知のスキーマでも強力な汎化能力を発揮し、ゼロショット設定および低リソース設定ではそれぞれ最大12.5%および21.9%向上しました。"

Quotes

"After training on billions of automatically annotated data and refining with human-annotated IE datasets, Know-Coder demonstrates remarkable performance improvements on different IE tasks under the various evaluation settings."

Key Insights Distilled From

KnowCoder

by Zixuan Li,Yu... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.07969.pdf

Deeper Inquiries

How does the code-style schema representation method in KnowCoder enhance the understanding and extraction of structured knowledge compared to traditional methods

KnowCoder's code-style schema representation method enhances the understanding and extraction of structured knowledge compared to traditional methods in several ways. Firstly, by representing schemas as Python classes with clear definitions, examples, and constraints, KnowCoder provides a more intuitive and comprehensive way for Large Language Models (LLMs) to understand different concepts. This allows LLMs to grasp complex relationships among entities, relations, and events more effectively.
Secondly, the use of class inheritance in the code-style schema representation helps capture taxonomies within schemas. By defining hierarchies of concepts through class inheritance, KnowCoder enables LLMs to understand the relationships between different types of knowledge better. This hierarchical structure aids in organizing information and guiding the extraction process.
Additionally, incorporating type hints in the initialization functions of classes allows for strict modeling of constraints among different concepts. This ensures that LLMs follow specific guidelines when extracting structured knowledge from text data. The inclusion of class methods further refines extracted results based on task-specific criteria or post-processing requirements.
Overall, the code-style schema representation method in KnowCoder offers a more systematic and detailed approach to structuring schemas for universal information extraction tasks. It provides a solid foundation for LLMs to comprehend diverse types of knowledge accurately and extract structured information efficiently.

What are the potential limitations of using automatically generated data for pretraining in large language models like KnowCoder

Using automatically generated data for pretraining large language models like KnowCoder comes with potential limitations that need to be considered:

Quality Control: Automatically generated data may contain noise or inaccuracies due to errors in data collection processes or imperfect algorithms used for generation. This can lead to incorrect annotations or misleading patterns being learned by the model during pretraining.

Domain Specificity: The automatically generated data may not cover all possible scenarios or edge cases present in real-world datasets across various domains. As a result, the model's generalization ability could be limited when faced with unseen instances during inference.

Bias Amplification: Biases present in the training data used for automatic generation can get amplified during pretraining if not properly addressed beforehand. This could lead to biased predictions by the model on sensitive topics or underrepresented groups.

4Data Diversity: Automatically generated datasets might lack diversity compared to human-curated datasets since they are often created using predefined rules or heuristics rather than natural variations found in real-world text corpora.

How can the two-phase learning framework in KnowCoder be applied to other domains beyond information extraction for improved performance

The two-phase learning framework employed by KnowCoder can be applied beyond information extraction domains for improved performance in various tasks requiring structured knowledge processing:
1**Scientific Research: In scientific research fields such as biology or chemistry where understanding complex relationships between entities is crucial (e.g., protein interactions), adapting KnowCoder's framework could enhance automated literature analysis and hypothesis generation based on textual sources.
2**Legal Industry: Legal document analysis often involves extracting key entities (e.g., laws, regulations) and their relationships from vast amounts of legal texts.
3**Healthcare: Medical records contain valuable patient information that needs accurate extraction; applying KnowCoders' framework could improve medical entity recognition tasks like identifying diseases mentioned alongside treatments.
By customizing schema representations according
to domain-specific requirements 、the two-phase learning
framework can help train models effectively across
different industries while ensuring accurate
extraction 、and interpretationofstructuredknowledge。