Artificial intelligence isn’t always a reliable source of information: large language models (LLMs) like Llama and ChatGPT can be prone to “hallucinating” and inventing bogus facts. But what if AI could be used to detect mistaken or distorted claims, and help people find their way more confidently through a sea of potential distortions online and elsewhere?
As presented at a workshop at the annual conference of the Association for the Advancement of Artificial Intelligence, researchers at Stevens Institute of Technology present an AI architecture designed to do just that, using open-source LLMs and free versions of commercial LLMs to identify potential misleading narratives in news reports on scientific discoveries.
“Inaccurate information is a big deal, especially when it comes to scientific content—we hear all the time from doctors who worry about their patients reading things online that aren’t accurate, for instance,” said K.P. Subbalakshmi, the paper’s co-author and a professor in the Department of Electrical and Computer Engineering at Stevens.
“We wanted to automate the process of flagging misleading claims and use AI to give people a better understanding of the underlying facts.”
To achieve that, the team of two Ph.D. students and two Master’s students led by Subbalakshmi first created a dataset of 2,400 news reports on scientific breakthroughs.
The dataset included both human-generated reports, drawn either from reputable science journals or low-quality sources known to publish fake news, and AI-generated reports, of which half were reliable and half contained inaccuracies.
Each report was then paired with original research abstracts related to the technical topic, enabling the team to check each report for scientific accuracy. Their work is the first attempt at systematically directing LLMs to detect inaccuracies in science reporting in public media, according to Subbalakshmi.
“Creating this dataset is an important contribution in its own right, since most existing datasets typically do not include information that can be used to test systems developed to detect inaccuracies ‘in the wild,'” Dr. Subbalakshmi said. “These are difficult topics to investigate, so we hope this will be a useful resource for other researchers.”
Next, the team created three LLM-based architectures to guide an LLM through the process of determining a news report’s accuracy. One of these architectures is a three-step process. First, the AI model summarized each news report and identified the salient features.
Next, it conducted sentence-level comparisons between claims made in the summary and evidence contained in the original peer-reviewed research. Finally, the LLM made a determination as to whether the report accurately reflected the original research.
The team also defined “dimensions of validity” and asked the LLM to think about these five “dimensions of validity”—specific mistakes, such as oversimplification or confusing causation and correlation, commonly present in inaccurate news reports.
“We found that asking the LLM to use these dimensions of validity made quite a big difference to the overall accuracy,” Dr. Subbalakshmi said and added that these dimensions of validity can be expanded upon, to better capture domain-specific inaccuracies, if needed.
Using the new dataset, the team’s LLM pipelines were able to correctly distinguish between reliable and unreliable news reports with about 75% accuracy—but proved markedly better at identifying inaccuracies in human-generated content than in AI-generated reports. The reasons for that aren’t yet clear, although Dr. Subbalakshmi notes that non-expert humans similarly struggle to identify technical errors in AI-generated text.
“There’s certainly room for improvement in our architecture,” Dr. Subbalakshmi says. “The next step might be to create custom AI models for specific research topics, so they can ‘think’ more like human scientists.”
In the long run, the team’s research could open the door to browser plugins that automatically flag inaccurate content as people use the Internet, or to rankings of publishers based on how accurately they cover scientific discoveries.
Perhaps most importantly, Dr. Subbalakshmi says, the research could also enable the creation of LLM models that describe scientific information more accurately, and that are less prone to confabulating when describing scientific research.
“Artificial intelligence is here—we can’t put the genie back in the bottle,” Dr. Subbalakshmi said. “But by studying how AI ‘thinks’ about science, we can start to build more reliable tools—and perhaps help humans to spot unscientific claims more easily, too.”
More information:
Yupeng Cao et al, CoSMis: A Hybrid Human-LLM COVID Related Scientific Misinformation Dataset and LLM pipelines for Detecting Scientific Misinformation in the Wild. openreview.net/pdf/17a3c9632a6 … f245c9dce44cf559.pdf
Stevens Institute of Technology
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Team teaches AI models to spot misleading scientific reporting (2025, May 29)
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