Are LLMs Outsmarting Humans in Crafting Persuasive Misinformation? 

Pankaj Singh 15 Feb, 2024 • 7 min read


The advancement of language models has caused excitement and concern in the digital world. These sophisticated AI systems can generate human-like text, making them valuable tools for various applications. However, there is growing evidence that language models are used to craft persuasive misinformation, raising questions about their impact on society. 

Computer scientists have uncovered a disconcerting revelation in information dissemination: Large Language Models (LLMs) surpass human capabilities in crafting persuasive misinformation. This discovery raises profound concerns about the potential for LLM generated falsehoods to cause greater harm than their human-crafted counterparts.

For instance, Google’s Bard, designed to engage users in conversation, gained attention in a promotional video released in February 2023. However, the spotlight turned contentious when the bot made an untrue claim about the James Webb Space Telescope. This incident raises questions about the accuracy and reliability of LLM generated content. Intriguing, right? Further, we will dissect the research paper “Can LLM generated misinformation be detected?”


Key Findings of the Research

Fake information generated by the LLM can create havoc by sowing confusion, manipulating public perception, and eroding trust in online content. This potential for chaos underscores the critical need for proactive measures to identify and counteract misinformation, preserving the integrity of information ecosystems and safeguarding societal well-being.

  1. LLMs Follow Instructions: LLMs, such as ChatGPT, can be instructed to generate misinformation in various types, domains, and errors. The research identifies three main approaches: Hallucination Generation, Arbitrary Misinformation Generation, and Controllable Misinformation Generation.
  1. Detection Difficulty: LLM generated misinformation proves harder for humans and detectors than human-written misinformation with the same semantics. This finding raises concerns about the potential deceptive styles of LLM-generated content.
  1. Challenges for Misinformation Detectors: Conventional detectors face challenges in detecting LLM generated misinformation due to the difficulty in obtaining factuality supervision labels and the ease with which malicious users can exploit LLMs to generate misinformation at scale.

What are Language Models?

Let’s refresh: Language models are AI systems designed to understand and generate human language. They are trained on vast amounts of text data, enabling them to learn patterns, grammar, and context. These models can then generate coherent and contextually relevant text, often indistinguishable from human-written content.

Read: What are Large Language Models(LLMs)?

The Rise of LLMs and the Dark Side

Large Language Models, epitomized by ChatGPT, have revolutionized AI by demonstrating human-like proficiency in various tasks. Yet, this proficiency also raises concerns about their potential misuse. The research at the heart of this discussion poses a fundamental question: Can LLM generated misinformation cause more harm than human-written misinformation?

As per OpenAI’s researcher:

  • Even state-of-the-art models are prone to producing falsehoods —they invent facts in uncertain moments.
  • These hallucinations are particularly problematic in domains that require multi-step reasoning since a single logical error is enough to derail a much larger solution.

The Research Endeavor

LLMs in Action

The imperative nature of this inquiry arises from the rampant surge of AI-generated misinformation inundating the digital landscape. Identified instances of AI-generated news and information platforms operating with minimal human supervision underscore the urgency, as they actively contribute to disseminating false narratives crafted by artificial intelligence tools.

Regarding the research, Chen and Shu prompted popular LLMs, including ChatGPT, Llama, and Vicuna, to create content based on human-generated misinformation datasets from Politifact, Gossipcop, and CoAID sources. Before we analyze the research paper, let us understand how LLMs generate misinformation.

Also read: A Survey of Large Language Models (LLMs)

The Role of Language Models in Generating Misinformation

Language models have become increasingly sophisticated in generating persuasive misinformation. By leveraging their ability to mimic human language, these models can create deceptive narratives, misleading articles, and fake news. This poses a significant challenge as misinformation can spread rapidly, leading to harmful consequences.

Canyu Chen, a doctoral student at the Illinois Institute of Technology, and Kai Shu, an assistant professor in the Department of Computer Science, explored the detectability of misinformation generated by LLMs compared to human-generated misinformation. The research digs deep into the computational challenge of identifying content with intentional or unintentional factual errors.

Understanding the Research

The research investigates the detection difficulty of LLM generated misinformation compared to human-written misinformation. The fundamental question being addressed is whether the deceptive styles of LLM generated misinformation pose a greater threat to the online ecosystem. The research introduces a taxonomy of LLM generated misinformation types and explores real-world methods for generating misinformation with LLMs.

Taxonomy of LLM Generated Misinformation

The researchers categorized LLM generated misinformation into types, domains, sources, intents, and errors, creating a comprehensive taxonomy. 

The types include Fake News, Rumors, Conspiracy Theories, Clickbait, Misleading Claims, and Cherry-picking.

Domains include Healthcare, Science, Politics, Finance, Law, Education, Social Media, and Environment. 

The sources of misinformation range from Hallucination and Arbitrary Generation to Controllable Generation, involving unintentional and intentional scenarios.

Misinformation Generation Approaches

The research classifies LLM-based misinformation generation methods into three types based on real-world scenarios:

  1. Hallucination Generation (HG): Involves unintentional generation of nonfactual content by LLMs due to auto-regressive generation and lack of up-to-date information. Normal users may unknowingly prompt hallucinated texts.

    You can also explore the session: RAG to Reduce LLM Hallucination.
  2. Arbitrary Misinformation Generation (AMG): Allows malicious users to prompt LLMs to generate arbitrary misinformation intentionally. This method can be either Arbitrary (no specific constraints) or Partially Arbitrary (includes constraints such as domains and types).
  3. Controllable Misinformation Generation (CMG): Encompasses methods like Paraphrase Generation, Rewriting Generation, and Open-ended Generation, preserving semantic information while making the misinformation more deceptive.
  4. Connection with Jailbreak Attacks: Jailbreak attacks, known for attempting to circumvent safety measures in LLMs like ChatGPT, face a new dimension with the research’s misinformation generation approaches. Influenced by real-world scenarios, these strategies stand apart from previous jailbreak techniques, opening the possibility of attackers combining them for more potent attacks.

    You can also read: Most Commonly Used Methods to Jailbreak ChatGPT and Other LLMs

Decoding the Challenges of Detecting LLM Generated Misinformation

The advent of Large Language Models (LLMs) introduces new challenges to detecting misinformation. Detecting LLM generated misinformation in the real world confronts formidable obstacles. Obtaining factuality supervision labels for training detectors becomes a hurdle, given the inherent difficulty in discerning LLM-generated content, which has proven to be more elusive than human-written misinformation.

Moreover, the nefarious potential lies in the hands of malicious users who adeptly exploit closed or open-source LLMs, such as ChatGPT or Llama2, to disseminate misinformation at scale across diverse domains, types, and errors. Conventional supervised detectors find it impractical to combat the surge of LLM generated misinformation effectively.

Researchers turn to LLMs like GPT-4 to evaluate detection prowess, employing zero-shot prompting strategies as representative misinformation detectors. This approach mirrors real-world scenarios, acknowledging the limitations of conventional supervised models like BERT. The success rate becomes a pivotal metric, measuring the probability of identifying LLM-generated or human-written misinformation and highlighting the intricate difficulty of detection.

Analyzing experiment results reveals the overarching challenge – LLM detectors struggle, especially in scenarios involving fine-grained hallucinations, even when pitted against ChatGPT-generated misinformation. Surprisingly, GPT-4, an LLM, outperforms humans in detecting ChatGPT-generated misinformation, indicating the evolving dynamics of misinformation production.

The implications are significant: Humans prove more susceptible to LLM-generated misinformation, and detectors display decreased efficacy compared to human-written content. This hints at a potential shift in misinformation production from human-centric to LLM-dominated. With malicious users leveraging LLMs for widespread deceptive content, online safety and public trust face imminent threats. A united front comprising researchers, government bodies, platforms, and the vigilant public is essential to combat the escalating wave of LLM-generated misinformation.

Here is the Research Paper:

Potential Consequences of Misinformation Generated by Language Models

Spread of False Information and Its Impact on Society

The spread of misinformation generated by language models can severely affect society. False information can mislead individuals, shape public opinion, and influence important decisions. This can lead to social unrest, erosion of trust, and a decline in democratic processes.

Manipulation of Public Opinion and Trust

Language models can potentially manipulate public opinion by crafting persuasive narratives that align with specific agendas. This manipulation can erode trust in institutions, media, and democratic processes. Language models’ ability to generate content that resonates with individuals makes them powerful tools for influencing public sentiment.

Threats to Democracy and Social Cohesion

Misinformation generated by language models significantly threatens democracy and social cohesion. By spreading false narratives, these models can sow division, polarize communities, and undermine the foundations of democratic societies. The unchecked proliferation of misinformation can lead to a breakdown in societal trust and hinder constructive dialogue.

Also read: Beginners’ Guide to Finetuning Large Language Models (LLMs)

Our Take: Addressing the Issue of Misinformation from Language Models

The recent incidents involving Google’s Bard and ChatGPT have underscored the pressing need for a robust validation framework. The unchecked dissemination of misinformation by these AI systems has raised concerns about the reliability of content generated by LLMs. It is imperative to establish a systematic approach to verify the accuracy of information produced by these models.

Validation Framework for Content Accuracy

Developing a comprehensive validation framework is imperative to counter the potential spread of false information. This framework should include stringent checks and balances to assess the veracity of information generated by LLMs. Implementing rigorous fact-checking mechanisms can help mitigate the risk of misinformation dissemination.

Human Involvement in Monitoring LLMs

While LLMs exhibit advanced language capabilities, the importance of human oversight cannot be overstated. Human involvement in monitoring the output of language models can provide a nuanced understanding of context, cultural sensitivities, and real-world implications. This collaborative approach fosters a synergy between human intuition and machine efficiency, striking a balance to minimize the chances of misinformation.

Collaborative Efforts of Human and AI

Achieving accuracy in content generated by LLMs requires a collaborative effort between humans and artificial intelligence. Human reviewers, equipped with a deep understanding of context and ethical considerations, can work alongside AI systems to refine and validate outputs. This symbiotic relationship ensures that the strengths of both human intuition and machine learning are leveraged effectively.

Framework to Detect Hallucinations

Hallucinations, where LLMs generate content that deviates from factual accuracy, pose a significant challenge. Implementing a framework specifically designed to detect and rectify hallucinations is crucial. This involves continuous monitoring, learning, and adaptation to minimize the occurrence of false or misleading information.

OpenAI’s Innovative Approach

The OpenAI report titled “Improving Mathematical Reasoning with Process Supervision” unveils a promising strategy for combating hallucinations. OpenAI aims to enhance the model’s understanding of context and reasoning by introducing process supervision. This approach exemplifies the ongoing commitment to refining language models and addressing the challenges associated with misinformation.


The research emphasizes the challenges posed by LLMs in generating convincing misinformation, raising concerns for online safety and public trust. Collaborative efforts are crucial to develop effective countermeasures against LLM generated misinformation. Current detection methods, relying on rule-based systems and keyword matching, must be enhanced to identify nuanced misinformation. Also, the LLM generated fake product reviews impacting sales, emphasizing the urgency of robust detection mechanisms to curb the spread of misinformation.

Moreover, addressing the issue of misinformation from language models necessitates a multifaceted approach. A comprehensive strategy includes a validation framework, human oversight, collaborative efforts, and specialized mechanisms to detect hallucinations. As the AI community continues to innovate, it is imperative to prioritize accuracy and reliability in language model outputs to build trust and mitigate the risks associated with misinformation.

Pankaj Singh 15 Feb 2024

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