The digital age has seen an increase in the creation and distribution of false information, made easier by the rise of online news outlets and social media platforms. This rise of disinformation affects public trust and the accuracy of data in important sectors like healthcare and finance, making the fight against disinformation important. Large Language Models (LLMs) like ChatGPT and GPT-4 provide an opportunity to tackle this issue.
On the one hand, LLMs offer multiple advantages to combat disinformation. They have a vast knowledge base, superior reasoning abilities, and can act independently by integrating external information and data. However, they have a downside. Due to their ability to mimic human speech, they can be programmed to produce false information, making the detection of misinformation challenging.
A recent study by the Illinois Institute of Technology presents an organized evaluation of the opportunities and risks related to using LLMs to combat misinformation. It suggests a paradigm-shift due to LLMs in misinformation detection, intervention, and attribution. It also highlights the inherent knowledge and reasoning capabilities of LLMs to legitimatize information and their potential capability to identify the source of misinformation.
The concept of intervention and attribution are significant in this battle. Intervention involves directly influencing the user, debunking false information after its spread (post-hoc intervention), or pre-emptively inoculating individuals against misinformation (pre-emptive intervention). Attribution, on the other hand, requires identifying the source of false information.
Although LLMs offer resources to combat misinformation, they can also generate highly convincing misinformation, posing potential risks in manipulation-prone sectors like politics and finance. The study suggests several solutions, including improving LLM safety through the selection of diverse, high-quality, and unbiased data for training LLMs. Enhanced transparency in how LLMs arrive at their outputs and implementing human oversight mechanisms are also recommended.
The study also suggests reducing ‘hallucinations’ (non-real information) generated by LLMs. This can be achieved by integrating fact-checking algorithms and external databases into the LLM generation process, developing techniques for LLMs to estimate the confidence they have in their outputs, and fine-tuning prompts to guide model outputs.
The researchers underline the need for a continuous and multifaceted approach to ensure LLMs are used ethically and responsibly in the fight against disinformation. They stress that collaboration between diverse stakeholders is vital to develop effective tools against the spread of false information.
