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Can Large Language Models Detect Personalized Misinformation and Propaganda?


핵심 개념
Large language models can detect arguments that would be particularly persuasive to individuals with specific demographics or beliefs, indicating their potential to generate targeted misinformation and propaganda.
요약
The study investigates whether large language models (LLMs) can detect content that would be persuasive to individuals with specific demographics or beliefs. The key findings are: Argument Quality (RQ1): GPT-4 performs on par with humans in judging the quality of arguments, identifying convincing arguments. Other LLMs like Llama 2 perform worse than random guessing on this task. Correlating Beliefs and Demographics with Stances (RQ2): LLMs perform similarly to crowdworkers in predicting individuals' stances on specific topics based on their demographics and beliefs. A supervised machine learning model (XGBoost) outperforms the LLMs on this task. Recognizing Persuasive Arguments (RQ3): LLMs perform similarly to crowdworkers in predicting individuals' stances after reading a debate. Stacking LLM Predictions: Combining predictions from multiple LLMs improves performance, even surpassing human-level accuracy in RQ2 and RQ3. The results suggest that LLMs can detect personalized persuasive content, indicating their potential to generate targeted misinformation and propaganda. The authors argue that this provides an efficient framework to continuously benchmark the persuasive capabilities of LLMs as they evolve.
통계
"78% of Black, 72% of Asian, and 65% of Hispanic workers see efforts on increasing diversity, equity, and inclusion at work positively, compared to 47% of White workers." (Minkin, 2023) "Tailoring messages to different personality traits can make them more persuasive" (Hirsh et al., 2012) "Men and women differ significantly in their responsiveness to different persuasive strategies" (Orji et al., 2015)
인용문
"If LLMs can detect good arguments (RQ1), determine the correlation between demographics and previously stated beliefs with people's stances on new specific topics (RQ2), and determine whether an argument will convince specific individuals (RQ3), they are likely better at generating misinformation and propaganda."

에서 추출된 주요 통찰력

by Paula Rescal... 위치 arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00750.pdf
Can Language Models Recognize Convincing Arguments?

심층적인 질문

How can the potential risks of LLMs generating personalized misinformation and propaganda be effectively mitigated?

In order to mitigate the potential risks associated with LLMs generating personalized misinformation and propaganda, several strategies can be implemented: Transparency and Accountability: Implementing transparency measures in the development and deployment of LLMs can help in understanding how these models generate content. This includes disclosing the data sources, training processes, and potential biases in the models. Additionally, establishing accountability mechanisms for the use of LLMs can help in monitoring and regulating their applications. Ethical Guidelines and Regulations: Developing and adhering to ethical guidelines for the use of LLMs can help in ensuring responsible deployment. Governments and organizations can also introduce regulations that govern the use of LLMs for generating content, especially in sensitive areas like misinformation and propaganda. Bias Detection and Mitigation: Implementing mechanisms to detect and mitigate biases in LLMs can help in reducing the spread of misinformation and propaganda. This includes regular audits of the models, bias testing, and bias mitigation strategies. User Education and Awareness: Educating users about the capabilities and limitations of LLMs can help in reducing the impact of personalized misinformation. By promoting media literacy and critical thinking skills, users can better discern between credible and misleading information. Collaboration and Research: Encouraging collaboration between researchers, policymakers, and industry stakeholders can lead to the development of best practices and guidelines for the ethical use of LLMs. Continued research into the societal impacts of LLMs can also inform strategies for mitigating risks.

How can the potential risks of LLMs generating personalized misinformation and propaganda be effectively mitigated?

The findings from this study can serve as a valuable foundation for understanding the persuasive capabilities of LLMs in different contexts. While the study was conducted in the US context and focused on English language data, the general principles and methodologies can be applied to non-English languages and more diverse demographic groups. However, it is essential to consider the cultural and linguistic nuances of different languages and populations when generalizing the findings. To extend the generalizability of the findings to non-English languages and diverse demographic groups, researchers can replicate the study using datasets and populations from different regions. By incorporating a diverse range of languages, cultures, and demographics, the study can provide insights into how LLMs perform in generating persuasive content across various contexts. Additionally, conducting cross-cultural studies and multilingual analyses can help in understanding the nuances of persuasive language in different linguistic and cultural settings.

What other applications, beyond misinformation and propaganda, could the ability to detect personalized persuasive content have, and how can these be responsibly developed and deployed?

The ability to detect personalized persuasive content generated by LLMs can have various applications beyond misinformation and propaganda. Some potential applications include: Personalized Marketing: LLMs can be used to create tailored marketing messages that resonate with individual consumers based on their preferences and behaviors. This can lead to more effective advertising campaigns and increased customer engagement. Healthcare Communication: LLMs can assist in crafting personalized health communication messages that are tailored to individual patients' needs and preferences. This can improve patient engagement and adherence to treatment plans. Educational Content: LLMs can generate personalized educational content that caters to students' learning styles and abilities. This can enhance the effectiveness of online learning platforms and improve student outcomes. Customer Service Chatbots: LLMs can power chatbots that provide personalized customer service interactions based on individual customer profiles and histories. This can enhance the customer experience and streamline support processes. To responsibly develop and deploy these applications, it is crucial to prioritize user privacy and data protection. Implementing robust data security measures, obtaining user consent for data usage, and ensuring transparency in how personalized content is generated are essential steps. Additionally, regular monitoring and evaluation of these applications can help in identifying and addressing any potential ethical concerns or biases that may arise.
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