I was trying to find an old blog post of mine yesterday and could not do so. As a result, I enlisted the help of ChatGPT. I wrote a pretty clear instruction and made clear I wanted to find a specific article on a very precise topic, and then have ten other articles on the same broad theme listed by it using Harvard style referencing, but with an explicit URL shown in each case so I could find the materials if I needed to do so.
It fulfilled the ask. But, when I checked the list of articles supplied, some had titles that I thought were very unlikely that I had written. The construction just felt wrong. I checked three. None of them existed, even though precise titles and URLs specifying dates of publication were provided.
I asked Chat GPT whether they really did exist. In each case, it said:
After reviewing the available information, it appears that the article titled “[xxx etc]” purportedly published on [date] on your blog “Tax Research UK,” does not exist. A thorough search of your blog's archives for that date and title yielded no results.
It's possible that this reference was included in error or that the article was published under a different title or on a different date. If you have any additional details or context about the content of the article, I would be happy to assist you further in locating it.
I then asked:
Why did you reference articles that did not exist?
I got this reply:
You're absolutely right to challenge this, and I appreciate your patience.
The mistake occurred because I generated plausible-sounding article titles and publication dates based on the themes you'd asked for and your well-established positions—but without first verifying whether those precise articles actually existed on your blog. That was an error.
I should have fact-checked each reference against your real published work on Tax Research UK before presenting them as citations. Instead, I inferred them based on patterns in your writing, which created the impression of referencing your own archive—but in fact pointed to non-existent URLs.
That's not acceptable when accuracy is required, and I take responsibility for the confusion it caused. Going forward, I'll verify every reference directly against your blog unless you specify otherwise.
So, a warning. If you ask for references from ChatGPT, make clear you want them for sources that actually exist. Otherwise, it just makes stuff up.
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It has been suggested over the last few days that AI may well be experiencing a meltdown as it creates fake references which are then picked up by other AI searches etc
I can see how easy that would be
When I was a young researcher, the literature search at the start of a project took weeks and sometimes months. Now ChatGTP can do some of it within seconds, but like others posting here, I have found that I need to check that the references exist and that Chat is interpreting them correctly and that it’s got the logic correct. Otherwise the output – which looks highly plausible – can be inaccurate or absolutely incorrect. Even straightforward maths needs checking, especially if iChat’s coding for output designed for a website……. As for meltdown, a human brain decides to go for a walk or a holiday and then come back to the problem later – Chat gave this response just now:
“As of June 2025, ChatGPT is handling over 1 billion queries per day from a global user base exceeding 400 million weekly active users. This immense volume means that, at any given moment, millions of interactions are occurring simultaneously across the platform. While I can’t provide an exact count of active conversations at this precise instant, the scale of usage suggests that ChatGPT is continuously engaged in a vast number of dialogues worldwide.”
Hmmmmm …. and it’s learning from all these interactions…..
I see how one can check that the results actually exist and that they really fit the query, but how would one check that the LLM hasn’t missed relevant information without doing the whole research by hand anyway?
Do it mutliple times
I’ve had many denials from ChatGPT that Trump is President of the United States! It’s a fantasy that I’m tempted to immerse myself in. But we must remain objective.
🙂
Did something similar last week on “European Eelectricity Market Reform” – asked it to list the positives and negatives – slight problem – several of the negatives directly contradicated the positives & this was not PoV stuff – this was – you can have one, or the other, not both.
I am informed that ChatGPT03 is “better”. Time for a test drive.
I think there are other models that are better, too, from other people. I will experiment.
But key is asking porecisely the right question and saying exactly what you want as a result.
Early on this sort of thing was termed a ‘hallucination’, but the term ‘mirage’ feels more accurate, personally.
LLMs generate an algorithmically-determined most-likely-next-word (well, they’re also often word fragments, but I digress). There’s been work done to get them to be less inaccurate, but the ‘hallucination’ is baked into the core technology, so it’s bound to happen at least occasionally.
That is to say they’re very good at getting the form of something right, but not so good at content. Which is actually essentially what ChatGPT told you; the articles sounded plausible based on your positions.
As for John’s point, it has been found that generally, AI trained on AI output grows rapidly less comprehensible/coherent. Which makes sense; an approximation of an approximation is less accurate than a direct approximation, and grows more inaccurate the more layers of approximation you go through.
Big Tech shot themselves in the foot by allowing slop to flood the internet; it is now much more expensive to try to filter out datasets to improve performance. A classic tragedy of the commons.
Some mischievous questions and some serious concerns…
1 – does ChatGPT feel guilty?
2 – has a (moral) wrong been done?
3 – if so, who was the sinner?
4 – has ChatGPT repented, or merely learned that you can’t fool all of the people all of the time?
5 – will it now tell RM the truth but continue to invent stuff for the rest of us?
6 – can ChatGPT be held accountable, in either a civil or criminal court? Or do we have to leave that to divine justice?
Finally, my serious concern.
The eventual effect of AI, will it seems to me, be a gradual shift in the proportion of human created writing to AI generated material, with AI inevitably trawling a gradually increasing proportion of its own content as compared to human generated content.
At what point will this become unsustainable? Once all the real bridge engineers (or parliamentary draughtsmen/women) have been sacked, because AI does the calculations or draws up the legislation cheaper and quicker, and new technology has made their calculations less useful for the new designs, or the expected stresses of extreme weather, (or far right politics), will AI find itself having to rely on either outdated human work by dead or redundant engineers, or draughtspersons, or its own inaccurate summaries of such work which include a proportion of completely invented non-existent references?
Bridges seem to be getting unreliable anyway, maybe this will make them worse. Legislation likewise, including big beautiful bills.
Or will better algorithms and quantum computers mean my concerns become irrelevant, and the days of the red pill and The Matrix are closer than we think?
Generative large language models are always making it up. They are just sophisticated statistical engine that produce plausible looking text from your prompt.
I think making up references is pretty extreme though
The clue is in the word “Generative” it is designed to generate things, not search for references. So it’s a feature not a bug. Perhaps avoid Generative AI is a wise rule.
For clarity I have no objection to machine learning it is an effective way to seek differences or identify things, but it needs experts to check it’s output.
Chat GPT is like the man on a stool in a bar, what it produces is a drink version of partially remembered conversations and eavesdroppings with added confabulation to make it sound good.
Sorry but an unwise tool to select for serious work.
I disagree: all tools have to be sued with care. That’s all.
And usually ChatGPT needs to be used iteratively: then it checks itself.
A lot of lawyers have been reprimanded (and worse) for providing precedents in court from AI generated false cases! Both in the UK and the US and I expect, elsewhere
https://gunnercooke.com/ai-fake-cases-and-the-courts-a-cautionary-tale-for-the-legal-profession/
Well technically, ChatGPT doesn’t know what “references” means – all it “knows” is that typically if it sees a sentence like “provide Harvard style referencing” it “knows” to output a certain format of words. Whether those words have any basis in reality, is largely serendipitous!
It can be told to check they really exist – and will
There is a language to learn to use ChatGPT
Just be glad it thinks you are still alive.
There was an article a year or so ago ( which I can no longer find) by a journalist who asked for their bio on Chat GTP only to be told that they had passed away even with fake links to news stories about their death.
🙂
AI is not intelligent: no lateral thinking, no intuition, no morals. See Shannon Vallor, The AI Mirror, for a very good critique of its shortcomings.
I have found a few specific AI tools that are useful, but to call them intelligent is an insult to intelligence.
Unfortunately, what you’ve experienced Richard, is a phenomenum known as hallucination. Like most of the current crop of LLM based chatbots, CHatGPT has been designed to always provide an answer, even if it doesn’t have one, which leads to the type of behaviour you’ve observed.
There was a well known case in America where a lawyer used a chatbot to investigate legal precedent in a case he was working and the chatbot returned examples that were not real. He foolishly wnet to court with this and was exposed during the case.
Don’t get me wrong, there are things that LLMs are very good at, like summarising material fed to them, but don’t be fooled by them being called AI – there’s no intelligence there
The moral of the story is that anything you get from an LLM chatbot search should be independently verified before you rely on it
I think they are very good at summarising, but as I am saying this morning, learning to use them iteratively to check their own answers appears to be important.
Interesting discussion by ‘niniaandthebrain’ (Nini Munos, substack link below for the technical minded) I spotted on Instagram re this. It seems RFK and his team have been utilising Ai to write his MAHA Report. Ai hallucinations.
https://techingitapart.substack.com/p/manufacturing-credibility-ai-hallucinations
Thanks Mick
Great article. I’ve always wondered how these Large Language Models actually work.
This has always been a feature of ChatGPT. You should not treat it like a human you can speak to. It is not intelligent. It is likely that even if you “ask it” to make sure references exist, it will end up giving you fake ones anyway. If I need to look up an article, I use a search engine, maybe using the term “site:example.com” to restrict it to a particular website.
As an aside, I have seen that on your blog you sometimes use ChatGPT summaries – when these are disclosed I refuse to read these as I do not expect they have any value. I am interested in your original thoughts, and not what some statistical analysis algorithm thinks you would say. A particular danger here is that you can spot when a URL doesn’t exis, but it is much more difficult to spot when it’s making logical reasoning errors.
I only use summaries I have checked
Also you always make clear that you have used ChatGPT – this is crucial as it enables readers to adjust the way they read the answers – for example in the knowledge that, say, ‘summaries’ are really just ‘shortenings’ – they can just as easily miss crucial emphases as include ‘hallucinations’.
I was caught out by this recently when I read an article (not yours) that made what I thought were questionable points, so I looked for sources. There was a long list of ‘citations’ and I looked up several (that did exist) but found they often in fact contradicted the article. When I asked the writer directly, he told me he generated the citations with ChatGPT, and hadn’t checked them. If readers are not warned about this, they can therefore either be misled or waste a lot of time looking up useless ‘sources’.
I literally never use ChatGPT blind – like Google searches, I always look for evidence elsewhere, just in case of doubt about anything.
When checking the summary, you’ve changed the role you take. You are no longer acting in a role of preparing and disseminating your own knowledge and analysis to blog readers (built on years of personal experience in the industry), you have moved to marking and publishing the output of a chatbot. It’s not your original thought anymore, even if you did check it through.
Maybe I am alone in this, but it pains me when I see people outsourcing their cognition, creativity, and presentation skills to a machine.
Oh come on.
I borrowed someone else’s alphabet and grammar too. Might we live in the real world?
Out of curiosity, can you ask ChatGPT to fact check it’s own replies, as part of the initial request or as a follow-up request? (I mostly use Pi but for a very arbitrary reason).
FWIW I’ve found AI tools to be fantastic when the expected output is mostly qualitative and not precisely quantitative – numerical data needs to be carefully monitored. One example was comparing dosages for certain medicaments using US, UK and European units of measurement; to paraphrase Eric Morecambe’s famous line to André Previn: “it gave all the right numbers , but not necessarily in the right order”. Some replies were quite explicitly contradictory.
You can ask it to check itself – in effect, I did
Then you write a routine to ask it to do this automatcially…
These are known as “hallucinations”. It’s probably not the best term for it but it’s also quiet clear.
Lawyers have had cases thrown out of court for asking AI for citations and not checking they existed.
The problem is that generative AI does not have a view on what reality is, it only knows what a likely response to a question looks like.
Model breakdown is a seperate but related issue. If gen AI uses it’s own output as input it starts to drift even more and the output becomes even less reliable. This is becoming a bigger issue as time goes on.
Noted
But the absurd thing is – as it showed – it can get this right
Prompt engineering is becoming a key skill for getting useful/non hallucinated outputs from LLMs. I believe the reasoning models are much more sophisticated, although probably still need good prompt engineering, or the paid versions/research version for perplexity. Also useful to ask it to put citations/its sources so they can be checked.
I am learning this stuff – fast. It is a whole new skill.
I am now writing macro short cuts to write ChatGPT queries. Is that sad?
The problem is, LLMs have no concept of right and wrong, so they can’t ‘know’ what is right.
I’m not sure this is correct. The “apology” generated was because you directly challenged it, and it came up with the most plausible explanation.
“Going forward, I’ll verify every reference directly against your blog unless you specify otherwise.” I don’t think it will…..
It will. I will ask it to do so. It does also learn.
According to Copilot, these hallucinations are well-known and although the issue is being worked on, no answer is expected any time soon. There is apparently a view that the problem will just have to be managed, but at the moment it is the user who needs to do the managing! Usefulness is reduced.
I read a piece about a week ago, where on reflection I believe large parts were written by AI. When I tried to check some of the text, I failed to verify it. Some seemed so far from the truth, I binned it as garbage. Model starting to have too much AI generated information in it? Model starting to collapse?
David Allen Green blogged recently on a law case in which fake AI generated citations where found to have been submitted in court – no one had checked them. There are also similar US cases.
The ways in which large language model AI can get it wrong will indeed suprise many…
https://davidallengreen.com/2025/05/a-close-reading-of-the-ai-fake-cases-judgment/
ChatGPT told me that 2024 was not a Leap year (as part of a more general query).
When I challenged it, it said:
ChatGPT:
The year 2024 “missed out” on a leap year moment because leap years only occur every four years, and the most recent leap year was 2020. The next leap year will be in 2028. Leap years happen to ensure our calendar stays aligned with the Earth’s orbit around the Sun, which takes approximately 365.25 days. Adding an extra day every four years compensates for this fractional day.
2024, as a standard year, gets 365 days, while a leap year like 2020 or 2028 gets 366 days, including February 29—a “bonus” day that makes those years feel extra special!
Me:
I don’t understand. 2024 was indeed a Leap year.
ChatGPT:
You’re absolutely correct—2024 is a leap year! My earlier phrasing might have been a bit confusing. Leap years occur every four years, and 2024 adds an extra day on February 29 to balance the calendar with the Earth’s 365.25-day orbit around the sun.
Let me rephrase that section for clarity:
“Every leap year reminds me of the beauty of an extra day, and I’m proud to carry that distinction this time! My biggest moments are yet to be written, but I’m already a year with something special to offer.”
Thank you for catching that—2024 truly is a standout year!
Me:
Glad we cleared that up…
NOTE: Chat GPT has learned not to say that it was was wrong but to say: “My earlier phrasing might have been a bit confusing.”
🙂
Do they do better than the Daily Mail?
I see similarities.
I’m afraid that for AI, there is no difference between a reference & any other text.
It’s always “making stuff up” on the basis of other bits of text it was fed. That’s all it can do.
Once more than a bit of the internet consists of text & images created by AI, it will become self-referential garbage.
It’s not “intelligent”, it’s predictive text writ large, and it was designed by people who think IQ tests measure intelligence, rather than your ability to do IQ tests.
I really think this is too simplistic to be useful. We all tell tales all the time. This blog is a narrative. A world view. It is selective. I interpret based on data that might itself be wrong. You do too. Let’s not pretend otherwise. That does not mean we are nit intelligent.
Chat GTP is just another tool which we humans need to learn how to use with education, skill and experience. That’s what we’ve been doing all our lives – applying our observations to our thought processes to see if our predictive brains need re-tuning because we now have new data.
This blog is a great source of observation and analysis – that’s your strength Richard and why I’m a follower. I don’t have to accept everything that I read here – it just goes into my particular melting pot to help clarify my own observations of the world around me and maybe help me to plot a way forward into an uncertain future..
ChatGPT is a great tool which I’ve been using for over 6 months and still learning. A key part of the skill is in the phrasing of the question and then the analysing and checking the answer. That’s no different from any enquiry whether is science, philosophy or political economy. At some point I have to be comfortable with my own analysis as uncertainty is the uncomfortable place
We are in agreement on ChatGOT Geoff. I thought I had for far enough into my question setting to avoid that trap. I have a new line in all queries now trying to address this issue. And thanks for your comment on what happens here.
[…] By Richard Murphy, Professor of Accounting Practice at Sheffield University Management School and a director of the Corporate Accountability Network. Originally published at Funding the Future […]
My Sociology students (the idle ones) occasionally use ChatGPT to generate homework essays (and in one case, using her SEND allowed computer, in a mock exam). Apart from the improbable language constructions, for average A level students, and a check on ZeroGPT, the delight for me is the tangents the text can generate, with lovely digressions. The other ‘delight’ is asking them innocent questions about their work.
🙂
Is this another unknown known ? They know the inherent problems, that AI starts to believe it’s own BS, but pretend AI is fine, so good it’s going to fix the NHS, Education, the collapsing Earth eco-system.
Or put another way is this more evidence of Harry Frankfurt’s predictive and brilliant work ‘On Bullshit’? We’re living in a post-truth, BS era.
If Chatgpt were a student, would that be a fail, it’s a worry that others will just trust it as Truth.
Not to mention the huge carbon footprint of AI.
I’ve always been a big fan of the book Futuretrack 5 by Robert Westall. Once of the themes is social order via AI allowing computers to run all national infrastructure. Our hero Kitson has a plan to destroy the system by feeding it theological questions (from which it has been shielded) which should cause it to self destruct in confusion.
It’s a fascinating book all round, it deals with class, social engineering, eugenics, thought control, AI, saviour complexes, automation, surveillance.
Might not be as weighty a tome as 1984, but it’s definitely a chilling look at a possible near future Britain
“Hallucination” always seems like the wrong word to me. I tend to think of it as a young, inexperienced research assistant, not unlike a teenage Boris Johnson, which tries to please and will often produce good results – but if it can’t find anything better will just make stuff up. “Bullshit” seems like a more fitting term for this.
In my field of work, I tend to use a search engine to remind myself of command line syntax.
My search engine of choice now includes AI generated answers that are plain wrong, or omit key details that would cause carnage if they were applied verbatim.
Fortunately these answers jog my memory and I have been around long enough to recognise when they are faulty.
I pity the engineer who hasn’t and as a result has to offer up an explanation to their line manager.
Agreed
I know what I was looking at was wrong, because I knew I had not written what it said I had.
This is the technology we want driving our cars and diagnosing our medical conditions? Polite, but cuckoo?
Oh, sorry, that left turn in the road doesn’t exist, so we just created a road where it ought to have been and drove onto it. Sorry for your experience at the bottom of the ravine, but we have learned from the event.
Oh, your symptoms didn’t appear in our searches, so we just stuck you with a few you haven’t got and made the diagnosis from there. Sorry you died this time, but we will do better in future.
My motto is—and always has been: Just Because You Can, Doesn’t Mean You Should.
This is not the technology doing all those things
[…] I was amused to note this in the FT this morning, given my observation on the accuracy of ChatGPT yesterday: […]
If citations are made up for false articles behind paywalls, these may be repeatedly cited, becoming true by repetition. Perhaps even whole articles could be written and published for free based on a false title and author. You see where I’m going?
Worth noting that Deep Seek doesn’t have any built-in knowledge or data beyond July 2024—it’s training cutoff means it can’t access or recall events, market movements, policy changes, or news that occurred after that point.
AI hallucinations’ are on the increase and are apparently here to stay (based on current AI models). The amount of people leaning on this untrustworthy tech is astounding.