Leveraging Large Language Models to Enhance Named Entity Recognition Datasets and Improve Model Performance

Core Concepts

This study proposes a novel hybrid approach that combines manual human annotation with Large Language Model (LLM)-based annotation to enhance the quality of Named Entity Recognition (NER) datasets. The method aims to recover from noise such as missed annotations in manually annotated datasets, while also addressing the issue of class imbalance in LLM-based annotations through a label mixing technique. The experiments demonstrate that this approach can achieve high performance in training NER models, even under constrained budget conditions.

Abstract

The study focuses on improving the quality of datasets used for training NER models, which is crucial for the performance and effectiveness of these models. The authors identify several challenges with the current manual annotation process, such as inconsistent quality, missed annotations, and high costs.
To address these issues, the study proposes a hybrid approach that combines manual human annotation with LLM-based annotation. The LLM-based annotation is used to supplement the missed annotations in the manually annotated datasets, aiming to recover from the noise introduced by these missed annotations.
The authors also observe that the LLM-based annotations can lead to an imbalance in the data volume per label, which can cause performance disparities among labels in the NER model. To mitigate this, they implement a label mixing technique that blends multiple assigned named entity labels for a single expression, ensuring the robustness of the NER model.
The experiments are conducted on the CoNLL03 and WikiGold datasets, and the results show that the proposed hybrid approach can achieve high performance in training NER models, even under constrained budget conditions. The authors also demonstrate the effectiveness of the label mixing technique in addressing the class imbalance issue.
Overall, the study presents a novel and cost-effective method for enhancing NER datasets, which can lead to improved performance of NER models in various applications.

Stats

The number of annotations for each entity type in the manual, noised, and LLM-based datasets shows a significant imbalance, with the "MISC" category having the fewest annotations.
The number of entities overlapping between classes in the CoNLL03 and WikiGold datasets is relatively small, with only 521 cases (2.65%) in CoNLL03 and 109 cases (6.80%) in WikiGold.

Quotes

"By utilizing a hybrid approach that combines manual annotations with annotations generated by large LLMs, we aim to recover from the noise introduced by missed annotations in the dataset, thereby enhancing the quality of the datasets used for training NER models at a low cost and in an automated manner."
"To address these issues, we implemented a label mixing technique for the multiple assigned named entity labels, thereby ensuring the robustness of the NER model by mixing these labels."

Key Insights Distilled From

Augmenting NER Datasets with LLMs

by Yuji Naraki,... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01334.pdf

Deeper Inquiries

How can the proposed hybrid approach be extended to other NLP tasks beyond named entity recognition?

The proposed hybrid approach, which combines manual human annotations with annotations generated by Large Language Models (LLMs), can be extended to various other NLP tasks beyond named entity recognition. One way to extend this approach is to apply it to tasks like sentiment analysis, text classification, machine translation, and question answering. By leveraging the strengths of LLMs in understanding and generating text, the hybrid approach can help improve the quality of datasets for these tasks as well. Additionally, the label mixing technique used in the hybrid approach can be adapted to handle different types of annotations required for tasks such as sentiment analysis or text classification. This extension can lead to more accurate models and better performance across a range of NLP tasks.

What are the potential biases and limitations of using LLMs for dataset annotation, and how can they be further mitigated?

Using LLMs for dataset annotation can introduce biases and limitations due to the nature of the models and the data they are trained on. One potential bias is the model's tendency to replicate biases present in the training data, leading to biased annotations. Additionally, LLMs may struggle with certain types of entities or contexts, resulting in inaccuracies in annotations. To mitigate these biases and limitations, several strategies can be employed. Firstly, diversifying the training data for LLMs can help reduce biases by exposing the models to a wider range of examples. Secondly, incorporating human oversight and validation in the annotation process can help correct errors and ensure the quality of annotations. Finally, regular monitoring and evaluation of the LLM-based annotations can help identify and address biases as they arise, improving the overall quality of the dataset.

How can the label mixing technique be adapted to handle more complex scenarios, such as overlapping entities or nested entities, to improve the overall robustness of NER models?

The label mixing technique, which blends two different entity labels for a single token to create new annotations, can be adapted to handle more complex scenarios in NER models. To address overlapping entities, the label mixing process can be modified to assign probabilities to each entity label based on their relevance to the token. This probabilistic approach can help the model learn to distinguish between overlapping entities more effectively.
For nested entities, where one entity is contained within another, the label mixing technique can be extended to consider hierarchical relationships between entities. By assigning different mixing ratios to entities based on their nesting levels, the model can learn to recognize and differentiate nested entities accurately. This adaptation of label mixing for complex scenarios can enhance the robustness and accuracy of NER models when dealing with overlapping or nested entities in text data.