This Illinois Institute of Technology AI report presents opportunities and challenges to combat misinformation with LLM

The spread of false information is a problem that has persisted in the modern digital age. The lowering of barriers to content creation and sharing brought about by the explosion of social media and online media has had the unintended consequence of accelerating the creation and distribution of different forms of disinformation (such as fake news and rumors) and amplify their impact on a global scale. Public trust in credible sources and the truth could be compromised due to the widespread dissemination of false information. Fighting misinformation is essential to protect information ecosystems and maintain public trust. This is particularly true in high-risk industries like healthcare and finance.

LLMs have been a paradigm shift in the fight against disinformation (e.g. ChatGPT, GPT-4). LLMs pose new opportunities and obstacles, making them a double-edged sword in the battle against misinformation. LLMs could radically disrupt existing paradigms of misinformation detection, intervention, and attribution due to their extensive knowledge of the world and superior reasoning abilities. LLMs can become increasingly powerful and even act as your agents by adding external information, tools and multimodal data.

However, studies have also shown that LLMs can be easily programmed to produce false information, intentionally or unintentionally, due to their ability to imitate human speech, which can include hallucinations, and their ability to follow human commands. Much more concerning, according to recent research, is that LLM-generated misinformation may have more misleading styles and possibly cause more harm than human-written misinformation with the same semantics. This makes it more difficult for humans and detectors to identify.

A new study by researchers at the Illinois Institute of technology presents a comprehensive and organized analysis of the possibilities and threats associated with combating disinformation in the era of LLM. They hope their work will encourage the use of LLM to combat misinformation and bring together stakeholders from diverse backgrounds to work together to combat misinformation generated by LLM.

Previous paradigms of misinformation detection, intervention, and attribution have begun to be revolutionized with the emergence of LLM to counter misinformation. The advantages that prove its adoption are the following:

• For starters, LLMs include a lot of global knowledge. The above benchmarks and related surveys show that LLMs can store much more knowledge than a single knowledge graph due to their billions of parameters and their pre-training on large corpora (e.g., Wikipedia). Therefore, LLMs can identify misleading writings that contain factual inaccuracies.
• LLMs are good reasoners, especially when it comes to zero-solution problems. They excel in symbolic reasoning, common sense reasoning, and mathematical reasoning. They can also break problems down into their components and reason using arguments in response to statements such as “Let's think step by step. ”As a result, LLMs can use their inherent knowledge to reason about the legitimacy of publications.
• LLMs can function as independent agents by incorporating external multimodal information, resources, tools and data. Hallucinations, in which the texts generated by LLMs contain information that is not real, are one of the main drawbacks of LLMs. Lack of access to current information and possible lack of understanding in specific sectors such as healthcare among LLMs is a major contributor to hallucinations. New studies have shown that using external knowledge or resources (such as Google) to obtain up-to-date information can help lessen the impact of LLM hallucinations.

The article highlights that the fight against misinformation could benefit from the two main strategies of large language models (LLM): intervention and attribution.

Dispel false claims and prevent their spread

Intervention involves directly influencing users rather than simply fact-checking. Disproving false information after it has already been spread is a strategy known as post-hoc intervention. There is potential for a backfire effect, where debunking could potentially reinforce belief in false information, even while LLMs could help create more compelling debunking messages. In contrast, preventative intervention vaccinates people against misinformation before they encounter it by using LLM to craft compelling “anti-misinformation” messages, such as pro-vaccination campaigns. Both approaches must take into account ethical considerations and the dangers of manipulation.

Find the original author: attribution

Another important part of the fight is attribution, which is figuring out where the false information comes from. Finding authors has traditionally depended on examining writing styles. Despite the lack of an LLM-based attribution solution, the remarkable power of LLMs to alter writing styles means they could be a game-changer in this area.

Human-LLM Association: an effective group

The team suggests that combining human knowledge with LLM capabilities can create an effective tool. By guiding the development of an LLM, humans can ensure that ethical considerations are prioritized and biases are avoided. LLMs can then support human decision-making and fact-checking with a wealth of data and analytics. The study calls for more research in this area to make the most of human and LLM strengths to counter misinformation.

Misinformation spread by LLM: a double-sided sword

While LLMs provide effective resources to combat misinformation, they also raise new challenges. LLMs have the potential to generate individualized misinformation that is both highly compelling and difficult to detect and refute. This presents dangers in areas where manipulation, such as politics and the financial sector, can have far-reaching effects. The study presents many solutions:

1. Improve LLM security:

• Data selection and bias mitigation: Training LLMs on carefully selected data sets that are diverse, high quality, and free of bias can help reduce the spread of misinformation. Techniques such as data augmentation and counterfactual training can also help address biases and misinformation present in existing data.
• Algorithmic transparency and explainability: Developing methods to understand how LLMs arrive at their results can help identify and address potential biases, hallucinations, and logical inconsistencies. This could involve creating interpretable models or developing tools that explain the reasoning behind the generated text.
• Human oversight and oversight mechanisms: Implementing human oversight mechanisms, such as fact-checking and content moderation, can help prevent the spread of false information generated by LLMs. Furthermore, the development of user interfaces that allow users to control the outcomes of LLMs, for example by specifying desired levels of factuality or objectivity, may allow users to interact with LLMs more critically.

2. Reduce hallucinations:

• Fact-checking and grounding in real-world data: Integrating fact-checking algorithms and knowledge bases into the LLM generation process can help ensure that results are consistent with real-world facts and evidence. This could involve verifying factual claims against external databases or incorporating factual constraints into model training objectives.
• Uncertainty awareness and confidence scoring: Training LLMs to be more aware of their limitations and uncertainties can help mitigate the spread of misinformation. This could involve developing techniques for LLMs to estimate how confident they are in their results and flag potentially unreliable information.
• Engineering and tuning cues: Carefully crafting cues and tailoring LLMs to specific tasks can help direct their results toward desired goals and reduce the likelihood of hallucinations. This approach requires understanding the specific context and desired outcomes of LLM use and designing prompts that guide the model toward generating accurate and relevant information.

The team emphasizes that there is no magic solution to address LLM safety and hallucinations. Implementing a combination of these approaches, along with continued research and development, is crucial to ensuring that LLMs are used responsibly and ethically in the fight against disinformation.

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