Cost-efficient Dataset Cleansing via LLM-based Data Annotation for Improving Multi-Document Summarization

핵심 개념

Leveraging large language models (LLMs) for cost-efficient dataset cleansing to enhance the quality of the Multi-News dataset for multi-document summarization.

초록

The researchers present a case study that extends the application of LLM-based data annotation to enhance the quality of the existing Multi-News dataset, a widely used dataset for multi-document summarization. The Multi-News dataset was constructed by crawling news articles from the internet, which often resulted in the inclusion of noisy and irrelevant documents.

To address this issue, the researchers designed a framework that utilizes LLMs to analyze the summary and associated documents, identifying and excluding any documents that are not relevant to the summary. Specifically, they employed approaches such as chain-of-thought (CoT) to provide the rationale for decision-making, enhancing transparency and facilitating human investigation. They further improved the cleansing process by incorporating self-consistency considerations, mimicking the majority voting process used by human annotators.

Based on this framework, the researchers introduced MULTI-NEWS+, an enhanced version of the Multi-News dataset, by removing the identified noisy documents. Experiments demonstrated that models trained on MULTI-NEWS+ outperformed those trained on the original Multi-News dataset, indicating the improved quality of the dataset. The researchers made MULTI-NEWS+ and the source code publicly available for further study.

This work showcases the potential of leveraging LLMs for cost-efficient dataset cleansing, which can be extended to other datasets across various domains, contributing to the advancement of natural language processing research.

통계

Starting in 1996, Alexa Internet has been donating their crawl data to the Internet Archive.
For the first time in decades, researchers trying to develop a vaccine for malaria have discovered a new target they can use to attack this deadly and common parasite.
Focused crawls are collections of frequently-updated webcrawl data from narrow (as opposed to broad or wide) web crawls, often focused on a single domain or subdomain.

인용구

"The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models."
"Cleansing these datasets contributes to enhancing model performance and generalization capabilities."
"Given the significance and necessity of enhancing the quality of existing datasets, these obstacles hinder practical efforts to cleanse datasets efficiently."

핵심 통찰 요약

Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation

by Juhwan Choi,... 게시일 arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09682.pdf

더 깊은 질문

How can the proposed LLM-based data annotation framework be extended to other types of datasets beyond multi-document summarization?

The LLM-based data annotation framework proposed in the study can be extended to various types of datasets beyond multi-document summarization by adapting the approach to suit the specific characteristics and requirements of different datasets. Here are some ways to extend the framework:

Dataset Specific Prompt Design: Tailoring the prompts used for LLM-based annotation to the specific nature of the dataset can enhance the effectiveness of the framework. By designing prompts that capture the key elements of the dataset, such as the type of information, context, and relationships within the data, the LLM can provide more accurate annotations.

Domain Adaptation: Adapting the LLM to different domains or types of datasets can improve its performance on diverse datasets. Fine-tuning the LLM on domain-specific data can enhance its understanding and annotation capabilities for datasets in those domains.

Task-specific Modifications: Modifying the LLM-based annotation process to align with the specific task or goal of the dataset can improve the quality of annotations. For instance, for datasets requiring sentiment analysis, the LLM can be trained to identify and annotate sentiment-related content accurately.

Incorporating Human Feedback: Integrating human feedback into the LLM-based annotation process can help improve the quality and accuracy of annotations. By incorporating human oversight and corrections, the LLM can learn from human input and enhance its annotation capabilities.

Multi-modal Data Annotation: Extending the framework to handle multi-modal datasets, such as those containing text, images, and videos, can broaden the applicability of LLM-based annotation. By incorporating multi-modal inputs, the LLM can provide more comprehensive annotations for diverse datasets.

Continuous Learning: Implementing a continuous learning approach where the LLM adapts and improves over time based on feedback and new data can enhance its performance on various types of datasets. By continuously updating the model with new information, the LLM can evolve to handle different dataset characteristics effectively.

How can the self-consistency and majority voting approaches used in this study be further improved or combined with other techniques to enhance the reliability and robustness of the dataset cleansing process?

The self-consistency and majority voting approaches used in the study can be further improved and combined with other techniques to enhance the reliability and robustness of the dataset cleansing process. Here are some strategies to enhance these approaches:

Ensemble Methods: Combining multiple LLM-based annotators and aggregating their annotations using ensemble methods, such as stacking or boosting, can improve the accuracy and reliability of the annotations. By leveraging the diversity of multiple annotators, ensemble methods can mitigate individual errors and biases.

Active Learning: Incorporating active learning techniques can enhance the self-consistency approach by selecting the most informative instances for human review. By prioritizing uncertain or challenging cases for human validation, active learning can improve the quality of annotations and reduce the need for extensive human involvement.

Adversarial Training: Implementing adversarial training techniques can enhance the robustness of the LLM-based annotators against adversarial attacks or noisy data. By training the LLM to resist perturbations and adversarial inputs, the model can provide more reliable annotations in the presence of challenging data.

Fine-tuning Strategies: Employing advanced fine-tuning strategies, such as curriculum learning or transfer learning, can improve the performance of the LLM-based annotators on specific datasets. By fine-tuning the LLM on dataset-specific tasks or domains, the model can adapt better to the characteristics of the data and enhance the quality of annotations.

Human-in-the-Loop Approaches: Integrating human-in-the-loop approaches, where human annotators provide feedback and corrections to the LLM annotations iteratively, can enhance the reliability of the dataset cleansing process. By combining human expertise with LLM-based annotations, the dataset quality can be improved effectively.

Explainable AI Techniques: Incorporating explainable AI techniques to provide insights into the decision-making process of the LLM-based annotators can enhance transparency and trust in the annotations. By making the annotation process more interpretable, stakeholders can better understand and validate the annotations, improving the overall reliability of the dataset cleansing process.