Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models

Bohan Jiang, Chengshuai Zhao, Zhen Tan, Huan Liu·June 26, 2024

Summary

This research paper investigates the detection of evolving disinformation from large language models (LLMs), particularly focusing on the challenges posed by models like ChatGPT. Traditional detection methods are insufficient due to LLMs' ability to produce coherent and contextually relevant disinformation. The authors introduce DELD (Detecting Evolving LLM-generated Disinformation), a parameter-efficient approach that combines pre-trained language models' fact-checking abilities with model-specific characteristics. DELD uses semantic embeddings and soft prompts to address label scarcity and enhance detection performance, outperforming existing methods. The study highlights the unique patterns of LLM-generated disinformation, contributes to the understanding of these models' capabilities, and emphasizes the need for tailored detection strategies as LLMs continue to evolve. It also touches on ethical considerations and the importance of responsible use in the context of combating disinformation.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of detecting evolving disinformation generated by large language models (LLMs) . This problem is relatively new and arises due to the continuous evolution of disinformation through the rapid development of LLMs and their variants . The detection of evolving LLM-generated disinformation presents unique challenges, such as the need to efficiently detect content generated by different LLMs without training separate models for each generator .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to detecting evolving disinformation generated using large language models (LLMs) . The focus is on understanding the impact of politically biased news on vaccine attitudes in social media, detecting LLM-generated misinformation, and evaluating algorithmic and human solutions to combat misinformation in the age of LLMs . The study also explores the deceptive power of LLM-generated fake news and the challenges associated with detecting such fake news in the real world .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models" proposes several new ideas, methods, and models related to disinformation detection and large language models (LLMs) .

  1. Evolving LLM-Generated Disinformation Detection: The paper addresses the challenging problem of evolving LLM-generated disinformation detection. It focuses on developing effective methods that can adapt to the constantly changing nature of disinformation generated by LLMs, ensuring robustness against future disinformation .

  2. Parameter-Efficient Fine-Tuning (PEFT): The paper introduces Parameter-Efficient Fine-Tuning as a significant approach to adapting large pre-trained models to specific tasks while minimizing computational and storage overhead. PEFT fine-tunes only a small subset of the model's parameters, reducing resource requirements and addressing issues like catastrophic forgetting .

  3. Low-Rank Adaptation of Large Language Models (LORA): The paper discusses LORA, a method for efficiently fine-tuning large language models. LORA focuses on adapting LLMs with low-rank techniques to improve efficiency in fine-tuning processes .

  4. Efficient Fine-Tuning of Quantized LLMs (QLORA): The paper introduces QLORA as an approach for efficient fine-tuning of quantized LLMs. This method aims to enhance the finetuning process of LLMs by optimizing the quantization techniques used .

  5. Llama-Adapter: The paper presents Llama-Adapter as an efficient method for fine-tuning language models with zero-init attention. This approach focuses on improving the fine-tuning process by incorporating zero-init attention mechanisms .

These proposed ideas, methods, and models contribute to the advancement of disinformation detection in the context of evolving LLM-generated content, providing innovative solutions to address the challenges posed by large language models in generating and detecting disinformation. The paper "Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models" introduces Parameter-Efficient Fine-Tuning (PEFT) as a significant advancement in adapting large pre-trained models to specific tasks while minimizing computational and storage overhead . Compared to traditional fine-tuning methods that update all model parameters, PEFT fine-tunes only a small subset of the model's parameters, reducing resource requirements and addressing issues like catastrophic forgetting . This approach achieves task-specific adaptation with reduced resource demands, making it more efficient and practical, especially for large language models (LLMs) with billions of parameters .

Furthermore, the paper explores the characteristics and advantages of PEFT in the context of evolving LLM-generated disinformation detection . By focusing on providing effective methods applicable to the constantly changing nature of LLM-generated disinformation, PEFT ensures robustness against future disinformation . The approach of fine-tuning only a small subset of parameters enhances the adaptability of LLMs to evolving disinformation, addressing the challenges posed by the iterative nature of LLM-generated content .

Moreover, the paper discusses the performance of DELD (Disinformation Evolving Language Detector) in detecting LLM-generated disinformation based on prompt length and position . The analysis reveals that a 12-token prompt prepended to the input sequence delivers the best performance for detecting LLM-generated disinformation, with an accuracy of 85.19% compared to 84.04% for appending . Placing prompts at the beginning of the input sequence (prepending) helps the model utilize prompt information more effectively, influencing the model's processing of subsequent tokens . This insight guides future research in optimizing prompt design for various natural language processing (NLP) tasks .

Additionally, the paper evaluates the robustness of DELD against different orders of dataset presentation during training . The method demonstrates consistency and robustness across various dataset orders, indicating that it is not biased or overly sensitive to the sequence in which data is presented . This robustness is crucial for real-world applications, as it helps mitigate potential biases that could arise from fixed data sequences .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of detecting evolving disinformation generated using large language models. Noteworthy researchers in this area include W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, B. Jiang, L. Cheng, Z. Tan, R. Guo, H. Liu, C. Chen, K. Shu, S. Wang, D. Lee, J. Zhou, Y. Zhang, Q. Luo, A. G. Parker, M. De Choudhury, G. Spitale, N. Biller-Andorno, F. Germani, A. Sliva, J. Tang, X. Zhou, R. Zafarani, X. Zhang, and A. A. Ghorbani .

The key to the solution mentioned in the paper involves understanding the impact of politically biased news on vaccine attitudes in social media, detecting llm-generated misinformation, evaluating algorithmic and human solutions for ai-generated misinformation, and fake news detection on social media from a data mining perspective .


How were the experiments in the paper designed?

The experiments in the paper "Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models" were designed to evaluate the performance of different disinformation detection methods across various experimental settings . The main experimental settings considered in the paper include:

  • Zero-Shot (No FT): Models without any fine-tuning showed lower performance, with ChatGPT achieving an average accuracy of 33.61% and the LLaMA model achieving 27.44% .
  • Fine-tuning on All Data: Training the model on combined data from all datasets resulted in improved accuracy, with BERT and T5 models achieving 76.47% and 78.39% accuracy, respectively. When enhanced with DELD, the accuracy further increased .
  • Fine-tuning a Model Per Dataset: Training a separate model for each dataset achieved an average accuracy of 76.89% for BERT and 78.81% for T5. The DELD method significantly outperformed the baselines in this setting, achieving the highest accuracy on specific datasets .
  • Fine-tuning on Sequential Datasets: Training models sequentially on each dataset to simulate the evolving nature of disinformation showed varying accuracies. The DELD method demonstrated robustness and effectiveness in detecting evolving disinformation, achieving higher accuracy compared to the baseline models .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation is the "Welfake dataset" for fake news detection in text data . The code for this dataset is open source and available online on Zenodo .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The research delves into the detection of evolving disinformation generated using large language models (LLMs) . The study explores the challenges posed by disinformation in the digital age and the advancements in automated techniques for disinformation detection, particularly focusing on LLM-generated disinformation . The experiments conducted in the paper demonstrate the effectiveness of detecting disinformation generated by LLMs, highlighting the complexities and nuances involved in identifying false information produced by these advanced language models .

The paper discusses the unique challenges posed by LLM-generated disinformation, emphasizing that such content can be more convincing and sophisticated compared to human-generated disinformation . The results of the experiments reveal that LLMs not only retain the core falsehoods but also enhance the narratives with persuasive and formal language, making it harder for human experts to detect such disinformation . This analysis supports the hypothesis that LLM-generated disinformation presents distinct detection challenges due to the models' text understanding and generation capabilities .

Moreover, the research highlights the evolving nature of LLM-generated disinformation and the need for advanced detection methods to combat the spread of false information . The experiments provide valuable insights into the detection of AI-generated disinformation across various domains, underscoring the importance of staying abreast of the rapid developments in LLM technology to effectively identify and mitigate the dissemination of false information . Overall, the experiments and results in the paper offer robust evidence supporting the scientific hypotheses related to detecting evolving disinformation generated using large language models.


What are the contributions of this paper?

The contributions of the paper include:

  • Providing a survey of large language models .
  • Understanding the impact of politically biased news on vaccine attitudes in social media .
  • Exploring the detection of misinformation generated by large language models .
  • Discussing disinformation, misinformation, and fake news in social media .
  • Evaluating AI-generated misinformation and assessing algorithmic and human solutions .
  • Highlighting the importance of balancing the reduction of false information with the preservation of freedom of expression .
  • Engaging with stakeholders to align goals with societal values and expectations .

What work can be continued in depth?

To delve deeper into the field of disinformation detection systems, it is crucial to focus on several key aspects:

  • Balancing False Positives: Enhancing the design of detection systems to minimize false positives is essential to ensure accurate identification of disinformation while avoiding unnecessary censorship of legitimate content .
  • Clear Explanations: Providing clear and transparent explanations for content flagged as disinformation is vital in maintaining trust and understanding among users and stakeholders .
  • Stakeholder Engagement: Continued engagement with stakeholders, including civil society organizations and the general public, is necessary to align the goals of disinformation detection systems with societal values and expectations .

Introduction
Background

1.1. Emergence of Large Language Models (LLMs) and their impact on disinformation 1.2. Challenges posed by LLMs, specifically ChatGPT, in disinformation generation

Objective

2.1. To address the inadequacy of traditional detection methods for LLM-generated disinfo 2.2. Introduce DELD: a parameter-efficient detection framework 2.3. Highlight the focus on semantic embeddings and soft prompts

Method
Data Collection

3.1. Data sources for LLM-generated disinformation 3.2. Collection of real and synthetic disinformation samples

Data Preprocessing

4.1. Cleaning and filtering of collected data 4.2. Handling label scarcity for LLM-generated content

DELD Framework

5.1. Combining pre-trained language models for fact-checking 5.2. Semantic embeddings for context understanding 5.3. Soft prompts for adapting to model-specific characteristics

Performance Evaluation

6.1. Comparison with existing detection methods 6.2. Metrics and experimental setup

Results and Analysis

7.1. Detection accuracy and effectiveness of DELD 7.2. Unique patterns in LLM-generated disinformation

Ethical Considerations and Responsible Use

8.1. Implications for responsible AI development 8.2. Guidelines for combating disinformation with evolving LLMs

Conclusion

9.1. Contributions to the understanding of LLM capabilities in disinfo 9.2. Future directions for tailored detection strategies 9.3. The significance of DELD in the evolving landscape of disinformation detection

Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the primary focus of the research paper discussed?
Why are traditional detection methods insufficient for large language models like ChatGPT?
What is the name of the approach introduced by the authors for detecting evolving LLM-generated disinformation?
How does DELD address the challenges of label scarcity in detecting disinformation?

Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models

Bohan Jiang, Chengshuai Zhao, Zhen Tan, Huan Liu·June 26, 2024

Summary

This research paper investigates the detection of evolving disinformation from large language models (LLMs), particularly focusing on the challenges posed by models like ChatGPT. Traditional detection methods are insufficient due to LLMs' ability to produce coherent and contextually relevant disinformation. The authors introduce DELD (Detecting Evolving LLM-generated Disinformation), a parameter-efficient approach that combines pre-trained language models' fact-checking abilities with model-specific characteristics. DELD uses semantic embeddings and soft prompts to address label scarcity and enhance detection performance, outperforming existing methods. The study highlights the unique patterns of LLM-generated disinformation, contributes to the understanding of these models' capabilities, and emphasizes the need for tailored detection strategies as LLMs continue to evolve. It also touches on ethical considerations and the importance of responsible use in the context of combating disinformation.
Mind map
Ethical Considerations and Responsible Use
Results and Analysis
Performance Evaluation
DELD Framework
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Method
Introduction
Outline
Introduction
Background

1.1. Emergence of Large Language Models (LLMs) and their impact on disinformation 1.2. Challenges posed by LLMs, specifically ChatGPT, in disinformation generation

Objective

2.1. To address the inadequacy of traditional detection methods for LLM-generated disinfo 2.2. Introduce DELD: a parameter-efficient detection framework 2.3. Highlight the focus on semantic embeddings and soft prompts

Method
Data Collection

3.1. Data sources for LLM-generated disinformation 3.2. Collection of real and synthetic disinformation samples

Data Preprocessing

4.1. Cleaning and filtering of collected data 4.2. Handling label scarcity for LLM-generated content

DELD Framework

5.1. Combining pre-trained language models for fact-checking 5.2. Semantic embeddings for context understanding 5.3. Soft prompts for adapting to model-specific characteristics

Performance Evaluation

6.1. Comparison with existing detection methods 6.2. Metrics and experimental setup

Results and Analysis

7.1. Detection accuracy and effectiveness of DELD 7.2. Unique patterns in LLM-generated disinformation

Ethical Considerations and Responsible Use

8.1. Implications for responsible AI development 8.2. Guidelines for combating disinformation with evolving LLMs

Conclusion

9.1. Contributions to the understanding of LLM capabilities in disinfo 9.2. Future directions for tailored detection strategies 9.3. The significance of DELD in the evolving landscape of disinformation detection

Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of detecting evolving disinformation generated by large language models (LLMs) . This problem is relatively new and arises due to the continuous evolution of disinformation through the rapid development of LLMs and their variants . The detection of evolving LLM-generated disinformation presents unique challenges, such as the need to efficiently detect content generated by different LLMs without training separate models for each generator .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to detecting evolving disinformation generated using large language models (LLMs) . The focus is on understanding the impact of politically biased news on vaccine attitudes in social media, detecting LLM-generated misinformation, and evaluating algorithmic and human solutions to combat misinformation in the age of LLMs . The study also explores the deceptive power of LLM-generated fake news and the challenges associated with detecting such fake news in the real world .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models" proposes several new ideas, methods, and models related to disinformation detection and large language models (LLMs) .

  1. Evolving LLM-Generated Disinformation Detection: The paper addresses the challenging problem of evolving LLM-generated disinformation detection. It focuses on developing effective methods that can adapt to the constantly changing nature of disinformation generated by LLMs, ensuring robustness against future disinformation .

  2. Parameter-Efficient Fine-Tuning (PEFT): The paper introduces Parameter-Efficient Fine-Tuning as a significant approach to adapting large pre-trained models to specific tasks while minimizing computational and storage overhead. PEFT fine-tunes only a small subset of the model's parameters, reducing resource requirements and addressing issues like catastrophic forgetting .

  3. Low-Rank Adaptation of Large Language Models (LORA): The paper discusses LORA, a method for efficiently fine-tuning large language models. LORA focuses on adapting LLMs with low-rank techniques to improve efficiency in fine-tuning processes .

  4. Efficient Fine-Tuning of Quantized LLMs (QLORA): The paper introduces QLORA as an approach for efficient fine-tuning of quantized LLMs. This method aims to enhance the finetuning process of LLMs by optimizing the quantization techniques used .

  5. Llama-Adapter: The paper presents Llama-Adapter as an efficient method for fine-tuning language models with zero-init attention. This approach focuses on improving the fine-tuning process by incorporating zero-init attention mechanisms .

These proposed ideas, methods, and models contribute to the advancement of disinformation detection in the context of evolving LLM-generated content, providing innovative solutions to address the challenges posed by large language models in generating and detecting disinformation. The paper "Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models" introduces Parameter-Efficient Fine-Tuning (PEFT) as a significant advancement in adapting large pre-trained models to specific tasks while minimizing computational and storage overhead . Compared to traditional fine-tuning methods that update all model parameters, PEFT fine-tunes only a small subset of the model's parameters, reducing resource requirements and addressing issues like catastrophic forgetting . This approach achieves task-specific adaptation with reduced resource demands, making it more efficient and practical, especially for large language models (LLMs) with billions of parameters .

Furthermore, the paper explores the characteristics and advantages of PEFT in the context of evolving LLM-generated disinformation detection . By focusing on providing effective methods applicable to the constantly changing nature of LLM-generated disinformation, PEFT ensures robustness against future disinformation . The approach of fine-tuning only a small subset of parameters enhances the adaptability of LLMs to evolving disinformation, addressing the challenges posed by the iterative nature of LLM-generated content .

Moreover, the paper discusses the performance of DELD (Disinformation Evolving Language Detector) in detecting LLM-generated disinformation based on prompt length and position . The analysis reveals that a 12-token prompt prepended to the input sequence delivers the best performance for detecting LLM-generated disinformation, with an accuracy of 85.19% compared to 84.04% for appending . Placing prompts at the beginning of the input sequence (prepending) helps the model utilize prompt information more effectively, influencing the model's processing of subsequent tokens . This insight guides future research in optimizing prompt design for various natural language processing (NLP) tasks .

Additionally, the paper evaluates the robustness of DELD against different orders of dataset presentation during training . The method demonstrates consistency and robustness across various dataset orders, indicating that it is not biased or overly sensitive to the sequence in which data is presented . This robustness is crucial for real-world applications, as it helps mitigate potential biases that could arise from fixed data sequences .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of detecting evolving disinformation generated using large language models. Noteworthy researchers in this area include W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, B. Jiang, L. Cheng, Z. Tan, R. Guo, H. Liu, C. Chen, K. Shu, S. Wang, D. Lee, J. Zhou, Y. Zhang, Q. Luo, A. G. Parker, M. De Choudhury, G. Spitale, N. Biller-Andorno, F. Germani, A. Sliva, J. Tang, X. Zhou, R. Zafarani, X. Zhang, and A. A. Ghorbani .

The key to the solution mentioned in the paper involves understanding the impact of politically biased news on vaccine attitudes in social media, detecting llm-generated misinformation, evaluating algorithmic and human solutions for ai-generated misinformation, and fake news detection on social media from a data mining perspective .


How were the experiments in the paper designed?

The experiments in the paper "Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models" were designed to evaluate the performance of different disinformation detection methods across various experimental settings . The main experimental settings considered in the paper include:

  • Zero-Shot (No FT): Models without any fine-tuning showed lower performance, with ChatGPT achieving an average accuracy of 33.61% and the LLaMA model achieving 27.44% .
  • Fine-tuning on All Data: Training the model on combined data from all datasets resulted in improved accuracy, with BERT and T5 models achieving 76.47% and 78.39% accuracy, respectively. When enhanced with DELD, the accuracy further increased .
  • Fine-tuning a Model Per Dataset: Training a separate model for each dataset achieved an average accuracy of 76.89% for BERT and 78.81% for T5. The DELD method significantly outperformed the baselines in this setting, achieving the highest accuracy on specific datasets .
  • Fine-tuning on Sequential Datasets: Training models sequentially on each dataset to simulate the evolving nature of disinformation showed varying accuracies. The DELD method demonstrated robustness and effectiveness in detecting evolving disinformation, achieving higher accuracy compared to the baseline models .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation is the "Welfake dataset" for fake news detection in text data . The code for this dataset is open source and available online on Zenodo .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The research delves into the detection of evolving disinformation generated using large language models (LLMs) . The study explores the challenges posed by disinformation in the digital age and the advancements in automated techniques for disinformation detection, particularly focusing on LLM-generated disinformation . The experiments conducted in the paper demonstrate the effectiveness of detecting disinformation generated by LLMs, highlighting the complexities and nuances involved in identifying false information produced by these advanced language models .

The paper discusses the unique challenges posed by LLM-generated disinformation, emphasizing that such content can be more convincing and sophisticated compared to human-generated disinformation . The results of the experiments reveal that LLMs not only retain the core falsehoods but also enhance the narratives with persuasive and formal language, making it harder for human experts to detect such disinformation . This analysis supports the hypothesis that LLM-generated disinformation presents distinct detection challenges due to the models' text understanding and generation capabilities .

Moreover, the research highlights the evolving nature of LLM-generated disinformation and the need for advanced detection methods to combat the spread of false information . The experiments provide valuable insights into the detection of AI-generated disinformation across various domains, underscoring the importance of staying abreast of the rapid developments in LLM technology to effectively identify and mitigate the dissemination of false information . Overall, the experiments and results in the paper offer robust evidence supporting the scientific hypotheses related to detecting evolving disinformation generated using large language models.


What are the contributions of this paper?

The contributions of the paper include:

  • Providing a survey of large language models .
  • Understanding the impact of politically biased news on vaccine attitudes in social media .
  • Exploring the detection of misinformation generated by large language models .
  • Discussing disinformation, misinformation, and fake news in social media .
  • Evaluating AI-generated misinformation and assessing algorithmic and human solutions .
  • Highlighting the importance of balancing the reduction of false information with the preservation of freedom of expression .
  • Engaging with stakeholders to align goals with societal values and expectations .

What work can be continued in depth?

To delve deeper into the field of disinformation detection systems, it is crucial to focus on several key aspects:

  • Balancing False Positives: Enhancing the design of detection systems to minimize false positives is essential to ensure accurate identification of disinformation while avoiding unnecessary censorship of legitimate content .
  • Clear Explanations: Providing clear and transparent explanations for content flagged as disinformation is vital in maintaining trust and understanding among users and stakeholders .
  • Stakeholder Engagement: Continued engagement with stakeholders, including civil society organizations and the general public, is necessary to align the goals of disinformation detection systems with societal values and expectations .
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