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Combating Misinformation in the Age of LLMs: Opportunities and Challenges

Canyu Chen, Kai Shu

arXiv:2311.05656v1cs.CY

TL;DR

Misinformation threatens information ecosystems and public trust, while LLMs create both opportunities for detection and risks of deceptive generation at scale. This survey reviews pre-LLM detection research and synthesizes current efforts and outlooks for using LLMs against misinformation and addressing LLM-generated misinformation. It concludes that LLMs have substantial potential in this fight, but further efforts are needed to minimize associated risks.

  • Problem

    Misinformation threatens information ecosystems and public trust, creating a need to understand how LLMs can combat misinformation and how LLM-generated misinformation can be addressed.

  • Method

    The paper systematically reviews pre-LLM misinformation-detection techniques and surveys LLM opportunities, emerging threats, countermeasures, and future directions.

  • Results

    LLMs have great potential for combating misinformation, while additional efforts are needed to minimize risks from LLM-generated misinformation.

  • Takeaways & Limitations

    Addressing LLM-generated misinformation requires interdisciplinary measures from different stakeholders.

  • Takeaways & Limitations

    Trustworthy misinformation detection remains under exploration across robustness, explainability, fairness, privacy, and transparency.

Abstract

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Misinformation such as fake news and rumors is a serious threat on information ecosystems and public trust. The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double-edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emergent question is: how to utilize LLMs to combat misinformation? On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: how to combat LLM-generated misinformation? In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM-generated misinformation.

1 Introduction

Misinformation threatens information ecosystems and public trust, while LLMs introduce both promising capabilities and new risks. The survey reviews prior approaches and frames how LLMs can combat misinformation and how LLM-generated misinformation can be addressed.

  • 1 Introduction: Misinformation spreads more rapidly at scale as social media and online news reduce barriers to content generation and sharing.The resulting threat is especially serious in high-stakes fields such as healthcare and finance.
  • 1 Introduction: LLMs are double-edged because they can generate human-like content, including hallucinated information, while following user instructions.This creates opportunities for combating misinformation alongside risks of misuse.
  • 1 Introduction: LLMs offer profound world knowledge and strong reasoning abilities that may improve misinformation detection, intervention, and attribution.They can also be augmented with external knowledge, tools, and multimodal information.
  • 1 Introduction: The survey systematically reviews pre-LLM misinformation-combatting research before examining LLM opportunities, challenges, current efforts, and future directions.Its two central questions concern utilizing LLMs to combat misinformation and combating misinformation generated by LLMs.

2 History of Combating Misinformation

Before LLMs, misinformation detection research developed scenario-specific methods spanning language, models, social context, knowledge, generalization, supervision, and multimodal information. The survey organizes this history into seven methodological classes.

  • 2 History of Combating Misinformation: Pre-LLM misinformation-detection methods are categorized into seven classes spanning linguistic features, neural models, social context, external knowledge, generalization, supervision cost, and multilingual or multimodal fusion.The classification is based on real-world detection scenarios.
  • 2.1 Capturing Linguistic Features: Linguistic approaches distinguish misinformation using stylistic, complexity, and psychological features, including length, vocabulary, sentiment, and sensational language.These features reflect patterns observed across misleading tweets and fake news.
  • 2.2 Leveraging Neural Models: Neural methods use models such as LSTM, CNN, and GNN for feature extraction, semantic correlation, and prediction.Reported applications include rumor and fake-news detection.
  • 2.3 Exploiting Social Context: Social-context methods incorporate user engagements and social networks because interactions and propagation patterns differ between fake and authentic news.Examples model comments, user-news environments, and related social signals.
  • 2.4–2.7: External-knowledge methods use knowledge graphs and evidential texts to examine article veracity through graph comparison or evidence-based reasoning.Other research addresses changing data distributions, limited labels, and multilingual or multimodal detection through adaptation, weak supervision, and information fusion.

3 LLMs for Combating Misinformation

LLMs may reshape misinformation combatting through world knowledge, reasoning, and augmentation with external resources and modalities. The survey examines detection opportunities and future uses in intervention, attribution, and human-LLM systems.

  • 3 LLMs for Combating Misinformation: The survey’s opportunity analysis asks whether LLMs can combat misinformation and reviews motivations, current detection efforts, and future directions.The outlook includes trustworthy detection, intervention, attribution, multimodal LLMs, agents, and human-LLM collaboration.
  • 3 LLMs for Combating Misinformation: LLMs’ broad knowledge and reasoning abilities motivate their use across misinformation detection, intervention, and attribution.Their capabilities are presented as a potential transformation of previous paradigms.
  • 3.1 Why Adopting LLMs?: LLMs contain substantial world knowledge from large pretraining corpora and billions of parameters, giving them potential to detect factual errors in misleading texts.ChatGPT’s understanding of medical terms illustrates this proposed capability.
  • 3.1 Why Adopting LLMs?: External knowledge, tools, multimodal information, and autonomous-agent capabilities can augment LLMs for misinformation-related tasks.Retrieved knowledge and tools can mitigate hallucinations, while multimodal reasoning supports images, code, tables, audio, and graphs.

3.2 LLMs for Misinformation Detection

Research has begun applying prompted and augmented LLMs to misinformation detection. These efforts use prompting, reasoning programs, retrieved evidence, tools, and LLM-generated supervision signals.

  • 3.2 LLMs for Misinformation Detection: Early studies directly prompt GPT-3, InstructGPT, ChatGPT-3.5, and GPT-4 for misinformation detection.Evaluations include standard prompting and zero-shot chain-of-thought strategies for human-written and LLM-generated misinformation.
  • 3.2 LLMs for Misinformation Detection: Program-guided fact-checking uses LLM in-context learning to generate reasoning programs that guide veracity verification.This illustrates a structured reasoning approach beyond direct classification.
  • 3.2 LLMs for Misinformation Detection: External knowledge and search or other tools can supplement LLM knowledge when detecting factual errors.Examples combine retrieved search-engine knowledge or multiple tools with LLM reasoning to predict claim veracity.
  • 3.2 LLMs for Misinformation Detection: Weakly supervised approaches use LLM-generated supervision signals to train misinformation detectors.Snorkel is one framework used for this purpose.

3.3 Outlook

The outlook extends LLM-based misinformation efforts beyond detection toward trustworthy multilingual and multimodal systems, intervention, attribution, and human-LLM collaboration.

  • 3.3.1 Trustworthy Misinformation Detection: Future detectors should harness multilingual and multimodal LLMs to improve effectiveness and trustworthiness.Multilingual LLMs generalize across languages, including low-resource languages, while multimodal systems integrate information across modalities.
  • 3.3.1 Trustworthy Misinformation Detection: Trustworthy detection remains unresolved across robustness, explainability, fairness, privacy, and transparency, despite LLM-generated explanations improving human-friendliness.The passage identifies these dimensions as essential for public trust and notes that aspects beyond explainability remain under study.
  • 3.3.2 Misinformation Intervention: LLMs could support misinformation intervention, which directly influences users beyond checking whether content is true.Intervention commonly follows fact-checking and debunking, distinguishing it from purely algorithmic detection.
  • 3.3.4 LLMs for Misinformation Attribution: LLMs show promise for misinformation attribution because they can identify and manipulate stylometric features, although no attribution works had yet adopted LLMs.Attribution can trace propaganda or conspiracy theories and hold publishers accountable.
  • 3.3.5 Human-LLM Collaboration: Human-LLM collaboration is proposed to combine human guidance and AI assistance in combating misinformation.Human guidance can reduce unintended harm, while AI can provide auxiliary information that supports analysis and decision-making.

4 Combating LLM-Generated Misinformation

The paper frames LLM-generated misinformation as an emerging challenge requiring characterization, field-specific threat analysis, countermeasures, and preparation for future risks.

  • 4 Combating LLM-Generated Misinformation: The section asks how to combat misinformation generated by LLMs and organizes the challenge around its characteristics, threats, countermeasures, and future risks.Countermeasures include alleviating hallucination, improving LLM safety, and detecting LLM-generated misinformation.
  • 4 Combating LLM-Generated Misinformation: The paper anticipates that misinformation from LLMs and other large generative AI models may produce more devastating real-world impacts in the future.It therefore discusses potential near-term risks and desired measures.
  • 4 Combating LLM-Generated Misinformation: The section covers emerging threats across journalism, healthcare, finance, and politics, considering misinformation’s deceptiveness and ease of production.These characteristics distinguish the threats examined in the field-specific discussion.

4.1 Characterization

LLM-generated misinformation is characterized by unintentional hallucinations and intentional malicious generation, with fabricated details and potentially more compelling deceptive content.

  • 4.1 Characterization: LLM-generated misinformation divides into unintentional generation and intentional generation according to users’ intents.Unintentional generation primarily refers to hallucinations, while intentional generation involves knowingly prompting models to produce misinformation.
  • 4.1 Characterization: Hallucinations can arise from autoregressive generation and lack of up-to-date information, including when users have no malicious intent.They may appear during ordinary prompting rather than deliberate misuse.
  • 4.1 Characterization: Prompts requesting short news can produce fabricated dates, names, addresses, numbers, and quotes even when the main message seems correct.The example identifies fabricated byline, date, and city details as fine-grained hallucinations.
  • 4.1 Characterization: Malicious users can prompt LLMs to generate fake news, rumors, conspiracy theories, clickbait, and misleading claims in deceptive styles.The passage links these styles to the potential for greater harm.
  • 4.1 Characterization: GPT-3 has been reported to generate accurate information that is easier to understand and misinformation that is more compelling.This contrast highlights a communication advantage that can apply to both accurate and misleading content.

4.2 Emerging Threats

LLM-generated misinformation threatens journalism, healthcare, finance, and politics because it can be produced easily and presented deceptively at scale.

  • 4.2 Emerging Threats: LLM-generated misinformation poses immediate threats across journalism, healthcare, finance, and politics.The section focuses on these fields because of misinformation’s deceptiveness and ease of production.
  • Journalism: 49 LLM-powered news websites in 7 languages were identified in April 2023, potentially producing hundreds of clickbait articles daily.The passage connects this production capacity with pollution of online information ecosystems and advertising-revenue optimization.
  • Journalism: More deceptive styles make LLM-generated misinformation difficult for readers, fact-checkers, and detection algorithms to distinguish from truth.Blurred boundaries between human-written and LLM-generated news could undermine trust in legitimate news sources.
  • Healthcare: LLMs can generate fabricated health articles that appear remarkably authentic, creating tools for health-misinformation generation and propagation.This risk accompanies the growing adoption of LLMs in healthcare applications.
  • Finance: Financial misinformation from LLMs escalates existing risks because bad actors can readily use models for disinformation campaigns and counterfeit content.Existing financial misinformation can disrupt markets, mislead investors, and amplify economic instability.
  • Politics: In politics, LLMs can be weaponized to generate deceptive narratives about candidates, policies, or events at scale, potentially distorting democracy.The passage presents this as a new age of political misinformation and disinformation.

4.3 Countermeasures

The paper surveys four countermeasures against LLM-generated misinformation: reducing hallucinations, improving safety, detecting generated misinformation, and educating the public.

  • Alleviating hallucination: Hallucination mitigation addresses a major source of unintentional LLM-generated misinformation through training-stage and inference-stage approaches.Training methods include data curation and knowledge grounding, while inference methods include confidence or uncertainty estimation.
  • Improving safety: Safety guards aim to prevent malicious users from exploiting LLMs to generate harmful content, including misinformation.Prior work has evaluated and benchmarked the safety of various LLMs.
  • Detecting generated misinformation: Misinformation detection helps platforms prevent dissemination and increasingly addresses misinformation generated by neural models and LLMs.Methods for detecting LLM-generated texts and watermarking techniques are also attracting attention.
  • Public education: Public education should explain both LLMs’ capabilities and their limitations, including that coherent, plausible text may contain nonfactual information.This education may reduce unintentional misuse and hallucinated content generation by ordinary users.

4.4 Looking Ahead

The paper highlights emerging risks from multimodal generative AI, autonomous misinformation agents, and cognitive manipulation. It argues that addressing these risks requires coordinated work across technology, sociology, psychology, education, and policymaking.

  • AI-generated multimodal misinformation: Generative AI tools can be combined to create hyperrealistic but fabricated multimodal misinformation using text, audio, images, and video.Text-to-image systems and multimodal LLMs expand the tools available for producing such content.
  • Autonomous misinformation agents: LLM agents can automate complex tasks, while their safety guards may be broken through fine-tuning, creating risks from autonomous misinformation agents.The agents’ capabilities include planning, reasoning, executing, reflecting, and collaborating.
  • Cognitive security and AI-manipulation: LLM-generated misinformation could be weaponized as AI-powered cognitive attacks that exploit cognitive vulnerabilities to manipulate human behavior.The paper links this risk to concerns about humans’ cognitive security.
  • Cognitive security and AI-manipulation: Protecting people from the cognitive effects of LLM-generated misinformation remains under-explored.The paper also frames this misinformation as AI-manipulation or social media manipulation.
  • Interdisciplinary countermeasures: Long-term countermeasures span stronger factuality and safety, better detection, dissemination analysis, psychological intervention, digital literacy, and transparency regulations.The proposed agenda involves technology, sociology, psychology, education, and policymaking.

5 Conclusion

The survey reviews pre-LLM misinformation research and examines how LLMs may both support misinformation defenses and create new risks. It concludes that LLMs have substantial potential for combating misinformation, while further efforts are needed to reduce LLM-generated misinformation.

  • Conclusion: The survey systematically reviews misinformation-combating efforts before LLMs and discusses current efforts and future outlooks for both using and countering LLMs.Its two central questions concern utilizing LLMs to combat misinformation and combating misinformation generated by LLMs.
  • Conclusion: LLMs offer great potential in the fight against misinformation, but minimizing risks from LLM-generated misinformation requires more effort.The conclusion characterizes LLMs as bringing both opportunities and challenges.
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