Leveraging LLM-Generated Synthetic Data to Improve Stance Detection in Online Political Discussions

Core Concepts

Synthetic data generated from large language models can be effectively leveraged to improve the performance of stance detection models for online political discussions, either by augmenting the fine-tuning dataset or by using it in an active learning framework to reduce labeling effort.

Abstract

The paper presents two approaches to improve stance detection models for online political discussions using LLM-generated synthetic data:

Fine-tuning with synthetic data:

Augmenting the existing fine-tuning dataset with synthetic data related to the specific question can improve the performance of the stance detection model.
This helps ground the model to the specific question and overcome the challenge of limited data for certain questions.

Active learning with synthetic data (SQBC):

The authors propose a new active learning method called Synthetic Data-driven Query By Committee (SQBC) that uses the synthetic data as an oracle to identify the most informative unlabeled samples for manual labeling.
This can substantially reduce the labeling effort while maintaining or even improving the performance compared to using the full dataset.
The method outperforms random selection of samples for manual labeling.
Augmenting the active learning variants with synthetic data further boosts the performance.

The experiments on the X-Stance dataset show that both approaches can effectively improve the stance detection performance for online political discussions.

Stats

"Should insured persons contribute more to health costs (e.g. increase in the minimum deductible)?" has 500 samples in the train/test split.
"Do you support a general ban on advertising alcohol and tobacco?" has 106 samples in the train/test split.
"Should compulsory vaccination of children be introduced in accordance with the Swiss vaccination schedule?" has 196 samples in the train/test split.
"Should the residence permit for migrants from non-EU/EFTA countries be linked to the fulfilment of binding integration agreements throughout Switzerland?" has 181 samples in the train/test split.
"Should the federal government promote renewable energy more?" has 269 samples in the train/test split.

Quotes

"Stance detection is an important yet challenging task; it is important because the automated detection of stances can, e.g., improve discussion summarisation, facilitate the detection of misinformation, and provide more comprehensive evaluations of opinion distributions in online political discussions and participation processes."
"Fine-tuning transformer-based models to solve stance detection is a common practice, but training these models requires a large amount of annotated data."

Key Insights Distilled From

SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

by Stefan Sylvi... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08078.pdf

Deeper Inquiries

How can the proposed methods be extended to handle multi-class stance detection tasks with more than two classes?

In the context of multi-class stance detection tasks with more than two classes, the proposed methods can be extended by modifying the approach to accommodate the additional classes. One way to do this is by adjusting the labeling scheme to include the multiple classes and ensuring that the synthetic data generation process reflects the diversity of stances present in the dataset.
For multi-class tasks, the synthetic data generation process would need to be expanded to cover the various stances or categories involved. This could involve creating prompts that capture the nuances of each class and generating synthetic data based on these prompts. Additionally, the active learning framework would need to be adapted to handle the multiple classes by considering the uncertainty across all classes when selecting informative samples for manual labeling.
Furthermore, the fine-tuning process would need to be adjusted to account for the multi-class nature of the task. The classifier used in the stance detection model would need to be modified to support multi-class classification, such as using a softmax layer with multiple outputs corresponding to each class. By incorporating these adjustments, the proposed methods can effectively handle multi-class stance detection tasks with more than two classes.

What other NLP tasks beyond stance detection could benefit from leveraging LLM-generated synthetic data in an active learning framework?

Beyond stance detection, several other NLP tasks could benefit from leveraging LLM-generated synthetic data in an active learning framework. Some of these tasks include sentiment analysis, text classification, named entity recognition, and machine translation.

Sentiment Analysis: LLM-generated synthetic data can be used to create diverse sentiment-labeled datasets for training sentiment analysis models. Active learning with synthetic data can help in selecting the most informative samples for manual labeling, improving the model's performance.

Text Classification: Synthetic data can be generated to create labeled datasets for text classification tasks such as topic categorization, intent detection, or document classification. Active learning can assist in selecting representative samples for training the text classification models.

Named Entity Recognition (NER): Synthetic data can be utilized to generate text with annotated named entities for training NER models. Active learning can aid in identifying challenging cases for named entity recognition, enhancing the model's ability to recognize entities accurately.

Machine Translation: LLM-generated synthetic data can be used to create parallel corpora for training machine translation models. Active learning can help in selecting sentences that are difficult for the model to translate accurately, improving the translation quality.

By leveraging LLM-generated synthetic data in an active learning framework, these NLP tasks can benefit from enhanced model performance, reduced labeling effort, and improved generalization to diverse data.

What are the potential ethical considerations and risks associated with using LLM-generated synthetic data for tasks like stance detection in online political discussions?

When using LLM-generated synthetic data for tasks like stance detection in online political discussions, several ethical considerations and risks need to be addressed:

Bias and Fairness: LLMs may inadvertently amplify biases present in the training data, leading to biased synthetic data. This can result in unfair predictions and reinforce existing societal biases.

Misinformation: Synthetic data generated by LLMs may contain misinformation or misleading content, which can impact the accuracy and credibility of the stance detection model.

Privacy Concerns: LLMs trained on large datasets may inadvertently memorize sensitive information present in the data, raising privacy concerns when generating synthetic data.

Transparency and Accountability: The use of synthetic data can make it challenging to trace the origin of the generated content, leading to issues of transparency and accountability in the stance detection process.

Manipulation and Malicious Use: Synthetic data can be manipulated to influence the stance detection model's output, leading to potential misuse for spreading propaganda or disinformation in online political discussions.

Regulatory Compliance: Using synthetic data for sensitive tasks like stance detection may raise regulatory compliance issues related to data privacy, consent, and data protection laws.

To mitigate these risks, it is essential to implement robust validation processes, ensure transparency in the use of synthetic data, regularly audit the model for biases, and adhere to ethical guidelines and regulations governing the use of AI in sensitive domains like online political discussions. Additionally, involving domain experts and stakeholders in the development and deployment of stance detection models can help address ethical concerns and ensure responsible AI practices.