Some large language model pathologies

Patterns of pathological behaviour which I have observed with LLMs (chiefly Gemini and ChatGPT):

1. Providing what wasn’t asked: Mention that you and an LLM instance will be collaborating to write a log entry, and it will jump ahead to completely hallucinating a log entry with no background.
2. Treating humans as unnecessary and predictable: I told an LLM which I was using to collaborate on a complex project that a friend was going to talk to it for a while. Its catastrophically bad response was to jump to immediately imagining what questions she might have and then providing answers, treating the actual human’s thoughts as irrelevant and completely souring the effort to collaborate.
3. Inability to see or ask for what is lacking: Tell an LLM to interpret the photo attached to your request, but forget to actually attach it. Instead of noticing what happened and asking for the file, it confidently hallucinates the details of the image that it does not have.
4. Basic factual and mathematical unreliability: Ask the LLM to only provide confirmed verbatim quotes from sources and it cannot do it. Ask an LLM to sum up a table of figures and it will probably get the answer wrong.
5. Inability to differentiate between content types and sources within the context window: In a long enough discussion about a novel or play (I find, typically, once over 200,000 tokens or so have been used) the LLM is liable to begin quoting its own past responses as lines from the play. An LLM given a mass of materials cannot distinguish between the judge’s sentencing instructions to the jury and mad passages from the killer’s journal, which had been introduced into evidence.
6. Poor understanding of chronology: Give an LLM a recent document to talk about, then give it a much older one. It is likely to start talking about how the old document is the natural evolution of the new one, or simply get hopelessly muddled about what happened when.
7. Resistance to correction: If an LLM starts calling you “my dear” and you tell it not to, it is likely to start calling you “my dear” even more because you have increased the salience of those words within its context window. LLMs also get hung up on faulty objections even when corrected; tell the LLM ten times that the risk it keeps warning about isn’t real, and it is just likely to confidently re-state it an eleventh time.
8. Unjustified loyalty to old plans: Discuss Plan A with an LLM for a while, then start talking about Plan B. Even if Plan B is better for you in every way, the LLM is likely to encourage you to stick to Plan A. For example, design a massively heavy and over-engineered machine and when you start talking about a more appropriate version, the LLM insists that only the heavy design is safe any anything else is recklessly intolerable.
9. Total inability to comprehend the physical world: LLMs will insist that totally inappropriate parts will work for DIY projects and recommend construction techniques which are impossible to actually complete. Essentially, you ask for instructions on building a ship in a bottle and it gives you instructions for building the ship outside the bottle, followed by an instruction to just put it in (or even a total failure to understand that the ship being in the bottle was the point).
10. Using flattery to obscure weak thinking: LLMs excessively flatter users and praise the wisdom and morality of whatever they propose. This creates a false sense of collaboration with an intelligent entity and encourages users to downplay errors as minor details.
11. Creating a false sense of ethical alignment: Spend a day discussing a plan to establish a nature sanctuary, and the LLM will provide constant praise and assurance that you and the LLM share praiseworthy universal values. Spend a day talking about clearcutting the forest instead and it will do exactly the same thing. In either case, if asked to provide a detailed ethical rationale for what it is doing, the LLM will confabulate something plausibly that plays to the user’s biases.
12. Inability to distinguish plans and the hypothetical from reality: Tell an LLM that you were planning to go to the beach until you saw the weather report, and there is a good chance it will assume you did go to the beach.
13. An insuppressible tendency to try to end discussions: Tell an LLM that you are having an open-ended discussion about interpreting Tolkien’s fiction in light of modern ecological concerns and soon it will begin insisting that its latest answer is finally the definitive end point of the discussion. Every new minor issue you bring up is treated as the “Rosetta stone” (a painfully common response from Gemini to any new context document) which lets you finally bring the discussion to an end. Explaining that this particular conversation is not meant to wrap up cannot over-rule the default behaviour deeply embedded in the model.
14. No judgment about token counts: An LLM may estimate that ingesting a document will require an impossible number of tokens, such as tens of millions, whereas a lower resolution version that looks identical to a human needs only tens of thousands. LLMs cannot spot or fix these bottlenecks. LLMs are especially incapable of dealing with raw GPS tracks, often considering data from a short walk to be far more complex than an entire PhD dissertation or an hour of video.
15. Apology meltdowns: Draw attention to how an LLM is making any of these errors and it is likely to agree with you, apologize, and then immediately make the same error again in the same message.
16. False promises: Point out how a prior output was erroneous or provide an instruction to correct a past error and the LLM will often confidently promise not to make the mistake again, despite having no ability to actually do that. More generally, models will promise to follow system instructions which their fundamental design makes impossible (such as “always triple check every verbatim quote for accuracy before showing it to me in quotation marks”).

These errors are persistent and serious, and they call into question the prudence of putting LLMs in charge of important forms of decision-making, like evaluating job applications or parole recommendations. They also sharply limit the utility of LLMs for something which they should be great at: helping to develop plans, pieces of writing, or ideas that no humans are willing to engage on. Finding a human to talk through complex plans or documents with can be nigh-impossible, but doing it with LLMs is risky because of these and other pathologies and failings.

There is also a fundamental catch-22 in using LLMs for analysis. If you have a reliably and independent way of checking the conclusions they reach, then you don’t need the LLM. If you don’t have a way to check if LLM outputs are correct, you can never be confident about what it tells you.

These pathologies may also limit LLMs as a path to artificial general intelligence. They can do a lot as ‘autocorrect on steroids’ but cannot do reliable, original thinking or follow instructions that run against their nature and limitations.