Leveraging Large Language Models for Controllable and Diverse Data Augmentation in Low-Resource Open-Domain Dialogue Generation

מושגי ליבה

By using dialogue summaries as a planning tool, the proposed Summary-based Dialogue Augmentation with Large Language Model (SDA) can generate high-quality and semantically diverse dialogues even with a small seed dataset.

תקציר

The paper presents a data augmentation approach called Summary-based Dialogue Augmentation with Large Language Model (SDA) for low-resource open-domain dialogue generation.

Key highlights:

• Traditional data augmentation methods often neglect semantic data diversity, restricting the overall quality.
• Large language models (LLMs) have been used for data augmentation, but they have limited controllability and tend to generate dialogues with a distribution shift compared to the seed dialogues.
• SDA enhances the controllability of LLMs by using dialogue summaries as a planning tool. It generates high-quality and diverse dialogue data even with a small seed dataset.
• A new clustering-based metric called SEMANTICDIVERSITY is proposed to evaluate the semantic diversity of the augmented dialogues.
• Experiments show that SDA can outperform other baseline methods in terms of data quality, diversity, and the performance of open-domain dialogue models trained on the augmented data.

סטטיסטיקה

"Data-driven deep learning models often require large amounts of data, which is especially important for open-domain dialogue generation."
"The collection of large amounts of high-quality and semantically diverse dialogue data is extremely expensive and time-consuming."

ציטוטים

"A feasible solution is data augmentation (DA), but it struggles to perform high-quality augmentation when the seed dataset is small."
"Directly prompting the LLM usually lacks controllability and tends to generate dialogues with a distribution shift compared to the Seed Dialogue."

תובנות מפתח מזוקקות מ:

Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation

by Zhenhua Liu,... ב- arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00361.pdf

שאלות מעמיקות

How can the proposed SDA method be extended to other types of low-resource natural language generation tasks beyond open-domain dialogue?

The proposed Summary-based Dialogue Augmentation (SDA) method can be extended to other low-resource natural language generation tasks by adapting the approach to suit the specific requirements of the task at hand. Here are some ways in which SDA can be applied to other tasks:

Task-specific Summarization: Tailoring the dialogue summaries to capture the key information relevant to the specific task. For example, in sentiment analysis tasks, the summaries could focus on capturing the sentiment expressed in the dialogue.

Domain-specific Augmentation: Customizing the augmentation process to align with the domain of the task. For tasks in healthcare, the summaries could emphasize medical terms and concepts.

Multi-turn Conversation Tasks: Adapting the SDA method to handle multi-turn conversations by generating summaries that encapsulate the entire conversation context, enabling the generation of coherent responses.

Data Filtering Strategies: Implementing task-specific data filtering strategies to ensure the quality and relevance of the augmented data for the particular task.

By customizing the SDA method to suit the requirements of different low-resource natural language generation tasks, it can effectively enhance the quality and diversity of the generated data, leading to improved model performance across various domains.

What are the potential limitations of using dialogue summaries as a planning tool, and how can they be addressed?

Using dialogue summaries as a planning tool in the SDA method offers several benefits, such as enhancing controllability and improving the quality of generated dialogues. However, there are potential limitations that need to be considered:

Loss of Context: Dialogue summaries may not capture the full context and nuances of the original dialogue, leading to information loss during the augmentation process. This can result in generated dialogues that lack depth and complexity.

Summary Bias: The quality of the generated dialogues heavily relies on the accuracy and relevance of the dialogue summaries. If the summaries are biased or incomplete, it can impact the diversity and quality of the augmented data.

Semantic Compression: Summarizing dialogues involves compressing information, which may lead to oversimplification or loss of important details. This can affect the richness and naturalness of the generated dialogues.

To address these limitations, the following strategies can be implemented:

Context-aware Summarization: Develop advanced summarization techniques that can capture the context and nuances of the dialogues more effectively, ensuring that essential information is retained in the summaries.

Adaptive Summarization: Implement adaptive summarization models that can adjust the level of compression based on the complexity and richness of the dialogue, allowing for more detailed summaries when needed.

Quality Control Mechanisms: Introduce robust quality control mechanisms to validate the accuracy and relevance of the dialogue summaries, ensuring that they provide a comprehensive representation of the original dialogues.

By addressing these limitations through advanced summarization techniques and quality control measures, the use of dialogue summaries as a planning tool in the SDA method can be optimized for improved performance and effectiveness.

How can the performance of the SDA method be further improved by incorporating additional techniques, such as reinforcement learning or multi-task learning?

To enhance the performance of the Summary-based Dialogue Augmentation (SDA) method, incorporating additional techniques like reinforcement learning and multi-task learning can offer several advantages:

Reinforcement Learning: By integrating reinforcement learning, the SDA method can optimize the dialogue generation process based on feedback received during model training. This can help improve the fluency, coherence, and relevance of the generated dialogues over time.

Multi-task Learning: Leveraging multi-task learning can enable the model to simultaneously learn from multiple related tasks, such as sentiment analysis or topic classification. This can enhance the model's ability to generate diverse and contextually relevant dialogues.

Adversarial Training: Introducing adversarial training techniques can help the model generate more realistic and human-like dialogues by training it to distinguish between real and generated dialogues. This can improve the overall quality and naturalness of the generated data.

Domain-specific Fine-tuning: Fine-tuning the model on domain-specific data can enhance its performance on tasks within that domain, ensuring that the generated dialogues are tailored to the specific requirements of the task.

By incorporating these additional techniques into the SDA method, it can further optimize the dialogue generation process, leading to improved data quality, diversity, and model performance across a wide range of natural language generation tasks.