I am happy to share my latest publication at The American Economist: ChatGPT Hallucinates Non-existent Citations: Evidence from Economics
Citation: Buchanan, J., Hill, S., & Shapoval, O. (2024). ChatGPT Hallucinates Non-existent Citations: Evidence from Economics. The American Economist. 69(1), 80-87 https://doi.org/10.1177/05694345231218454
Blog followers will know that we reported this issue earlier with the free version of ChatGPT using GPT-3.5 (covered in the WSJ). We have updated this new article by running the same prompts through the paid version using GPT-4. Did the problems go away with the more powerful LLM?
The error rate went down slightly, but our two main results held up. It’s important that any fake citations at all are being presented as real. The proportion of nonexistent citations was over 30% with GPT-3.5, and it is over 20% with our trial of GPT-4 several months later. See figure 2 from our paper below for the average accuracy rates. The proportion of real citations is always under 90%. GPT-4, when asked about a very specific narrow topic, hallucinates almost half of the citations (57% are real for level 3, as shown in the graph).
The second result from our study is that the error rate of the LLM increases significantly when the prompt is more specific. If you ask GPT-4 about a niche topic for which there is less training data, then a higher proportion of the citations it produces are false. (This has been replicated in different domains, such as knowledge of geography.)
What does Joy Buchanan really think?: I expect that this problem with the fake citations will be solved quickly. It’s very brazen. When people understand this problem, they are shocked. Just… fake citations? Like… it printed out reference for papers that do not actually exist? Yes, it really did that. We were the only ones who quantified and reported it, but the phenomenon was noticed by millions of researchers around the world who experimented with ChatGPT in 2023. These errors are so easy to catch that I expect ChatGPT will clean up its own mess on this particular issue quickly. However, that does not mean that the more general issue of hallucinations is going away.
Not only can ChatGPT make mistakes, as any human worker can mess up, but it can make a different kind of mistake without meaning to. Hallucinations are not intentional lies (which is not to say that an LLM cannot lie). This paper will serve as bright clear evidence that GPT can hallucinate in ways that detract from the quality of the output or even pose safety concerns in some use cases. This generalizes far beyond academic citations. The error rate might decrease to the point where hallucinations are less of a problem than the errors that humans are prone to make; however, the errors made by LLMs will always be of a different quality than the errors made by a human. A human research assistant would not cite nonexistent citations. LLM doctors are going to make a type of mistake that would not be made by human doctors. We should be on the lookout for those mistakes.
ChatGPT is great for some of the inputs to research, but it is not as helpful for original scientific writing. As prolific writer Noah Smith says, “I still can’t use ChatGPT for writing, even with GPT-4, because the risk of inserting even a small number of fake facts… “
Follow-Up Research: Will Hickman and I have an incentivized experiment on trust that you can read on SSRN: Do People Trust Humans More Than ChatGPT?
@IMurtazashvili has pointed me to a great resource for AI-era literature review work. “AI-Based Literature Review Tools” from Texas A&M
Economist Writing Every Day 
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