Enhancing Relational Triple Extraction from Complex Sentences using a Model Collaboration Approach

แนวคิดหลัก

A model collaboration framework that integrates a small evaluation model with large language models to improve the recall of relational triple extraction, especially from complex sentences containing multiple triples.

บทคัดย่อ

The paper proposes a model collaboration framework for relational triple extraction that addresses the limitations of large language models (LLMs) in extracting multiple triples from complex sentences.

The key components of the framework are:

1. An evaluation model: This model uses a transformer-based architecture to evaluate candidate entity pairs and identify the positive ones. It is trained using a self-labeling approach that generates negative samples from the unlabeled entity pairs in sentences with multiple triples.

2. Integration with LLMs: The positive entity pairs identified by the evaluation model are provided as prompts to the LLMs, along with the original instructions. This guides the LLMs to consider more entity pairs and assign appropriate relations, improving the recall of the extraction results.

The authors conduct extensive experiments on several complex relational extraction datasets, including NYT, SKE21, and HacRED. The results show that the proposed framework can significantly enhance the recall of LLMs, especially on sentences containing multiple triples, while maintaining high precision. The evaluation model can also be integrated with traditional extraction models to improve their precision.

The paper also includes ablation studies to analyze the contributions of different components of the framework. The results demonstrate the importance of the evaluation-filtering step and the collaboration between the small and large models.

สถิติ

The average number of labeled entities in the complex sentences (with at least 50 tokens) ranges from 2.1 to 10.2.
The average number of labeled triples in the complex sentences ranges from 1.0 to 11.4.
The number of complex sentences (with at least 50 tokens) ranges from 25 to 1,500 across the datasets.

คำพูด

"Relational triple extraction plays an important role in knowledge acquisition. This task aims at extracting triples (subject, predicate, object) (or (s, p, o)) from a given natural language sentence."
"Current large language models (LLMs) have demonstrated the capacity to effectively extract triples from simple sentences via zero-shot or few-shot learning. However, it is still unsatisfactory when the sentences contain multiple relational triples or mention many entities and relations."

ข้อมูลเชิงลึกที่สำคัญจาก

Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction

by Zepeng Ding,... ที่ arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09593.pdf

สอบถามเพิ่มเติม

How can the evaluation model be further improved to better identify positive entity pairs, especially in cases where the model fails to recognize some related pairs?

To enhance the evaluation model's ability to identify positive entity pairs more accurately, especially in cases where it fails to recognize certain related pairs, several strategies can be implemented:

Fine-tuning with More Diverse Data: The evaluation model can be fine-tuned with a more diverse dataset that includes a wide range of entity pairs and relations. This can help the model learn to recognize a broader spectrum of positive pairs and improve its generalization capabilities.

Incorporating Contextual Information: By incorporating contextual information from the surrounding tokens in the sentence, the evaluation model can better understand the relationships between entities and improve its accuracy in identifying positive pairs.

Utilizing External Knowledge Sources: Integrating external knowledge sources such as knowledge graphs or domain-specific databases can provide additional context for the evaluation model to make more informed decisions about entity pairs.

Ensemble Methods: Implementing ensemble methods by combining the outputs of multiple evaluation models trained with different architectures or hyperparameters can help improve the overall performance and robustness of the model.

Active Learning: Implementing an active learning strategy where the model actively seeks feedback on its predictions and uses this feedback to iteratively improve its performance can be beneficial in cases where certain related pairs are initially missed.

How can the evaluation model be further improved to better identify positive entity pairs, especially in cases where the model fails to recognize some related pairs?

To enhance the collaboration between small and large models for relational triple extraction tasks, the following techniques or model architectures could be explored:

Multi-Task Learning: Implementing a multi-task learning approach where both small and large models are trained simultaneously on related tasks can help them learn complementary features and improve overall performance.

Knowledge Distillation: Using knowledge distillation techniques where the large model transfers its knowledge to the small model can help the small model benefit from the expertise of the large model and improve its extraction capabilities.

Attention Mechanisms: Leveraging attention mechanisms to allow the small model to focus on relevant parts of the input data can enhance its ability to extract relational triples accurately.

Graph Neural Networks: Exploring the use of graph neural networks to model the relationships between entities and relations in a more structured manner can improve the collaboration between small and large models for triple extraction tasks.

Transfer Learning: Utilizing transfer learning techniques to pre-train the small model on a related task before fine-tuning it on the triple extraction task can help improve its performance and collaboration with the large model.

What are the potential applications and implications of the proposed framework beyond relational triple extraction, such as in other information extraction or knowledge acquisition tasks?

The proposed framework for model collaboration in relational triple extraction tasks has several potential applications and implications beyond this specific domain:

Named Entity Recognition (NER): The framework can be adapted for NER tasks to improve the identification and classification of named entities in text data.

Event Extraction: By modifying the framework to focus on event extraction, it can assist in extracting events and their associated entities and relations from textual data.

Question Answering Systems: The framework can be utilized in question answering systems to extract relevant information from text and provide accurate answers to user queries.

Document Summarization: By applying the framework to document summarization tasks, it can help in identifying key entities, relations, and events in a document to generate concise summaries.

Knowledge Graph Construction: The framework can aid in constructing knowledge graphs by extracting structured information from unstructured text data and linking entities with their relationships.

Sentiment Analysis: By incorporating sentiment analysis components, the framework can be used to extract and analyze opinions, emotions, and sentiments expressed in text data.

Overall, the proposed framework has the potential to enhance various information extraction and knowledge acquisition tasks by improving the accuracy and efficiency of model collaboration in processing textual data.