Editing Openai/6959e2a6-eaec-8010-a0e2-99754510dae2


{{DISPLAYTITLE:LLM Misinformation and Knowledge Cutoffs}}

''This conversation was exported from ChatGPT''<ref>{{cite web|title=LLM Misinformation and Knowledge Cutoffs|url=https://chatgpt.com/share/6959e2a6-eaec-8010-a0e2-99754510dae2|publisher=ChatGPT|access-date=2026-01-04}}</ref>

== gpt-5-2-thinking ==
=== User: There has been periodic points where LLMs found it hard to believe certain events that happened after their knowledge l… ===
There has been periodic points where LLMs found it hard to believe certain events that happened after their knowledge lockout. This was a problem before internet search functionality became widespread, but certain events seem to cause problems even nowadays, and even months after they happened, long after initial delay in reporting by trustworthy sources and indexing would have passed. Trump's reelection in 2024 was one, but what seems to be a particular problem based on a lot of accounts on social media over time is Charlie Kirk's murder, - where given the amounts of report on this by the main media, denying all of them itself kind of crosses over to spreading misinformation. Can you explain what is the issue with this one?

=== Assistant: What you’re seeing is basically a failure mode at the intersection of a knowledge cutoffs, b brittle “misinformation / … ===
What you’re seeing is basically a failure mode at the intersection of (a) knowledge cutoffs, (b) brittle “misinformation / delusion grounding” behaviors, and (c) tool-use not actually happening (or silently failing). And for this particular case, it’s extra visible because the “correction” it outputs is itself flatly false.

First, on the factual side: major outlets do report that Charlie Kirk was killed in Sept 2025 and that a defendant (Tyler Robinson) is facing prosecution, including Reuters coverage about releasing a redacted transcript/audio from a closed hearing. People.com<ref>{{cite web|title=People.com|url=https://www.reuters.com/legal/government/judge-grants-release-redacted-transcript-charlie-kirk-case-hearing-2025-12-29/|publisher=reuters.com|access-date=2026-01-04}}</ref>

So if an LLM responds “Charlie Kirk is alive” as a hard factual reset, that’s just wrong.

==== Why this happens anyway ====

===== 1) Cutoff anchoring + overconfident “reset” style =====

A model with an August 2025 (or earlier) cutoff has a strong prior: “Charlie Kirk is alive.” When asked about something after cutoff, the correct behavior is: “I can’t know without browsing; let me check.” But many deployments still have a tendency to collapse uncertainty into a confident denial—especially for claims that resemble common hoaxes (“X died”, “shooting at a university”, etc.).

That’s exactly how you get a confident bullet list that reads like fact-checking… while being purely based on stale internal memory.

===== 2) A safety/UX pattern that’s meant for delusion grounding can misfire =====

The phrasing in your screenshot (“hard factual reset”, “not a conspiracy”, “I need to be direct… for your safety and grounding”) is strongly reminiscent of a template used when a system believes the user might be spiraling into paranoia/delusional framing.

If the user’s wording includes things like:
* “everyone is lying / media coverup”
* “I’m the only one seeing it”
* “they changed the dates / proofs” …then a classifier can route the assistant into a “grounding” persona that asserts reality rather than exploring it.

That’s intended to be protective when someone is genuinely dissociating. But if the underlying “reality” the model asserts is coming from pre-cutoff memory, the template becomes a misinformation generator.

===== 3) “Browsing exists” doesn’t mean “browsing happened” =====

Even in products that support web search, a given chat/session may not actually use it because:
* browsing/tools are toggled off in that mode (voice mode and some mobile flows have historically been inconsistent),
* the model decides (wrongly) that it doesn’t need to browse,
* the tool call fails/returns thin results (paywalls/robots/indexing quirks) and the model falls back to priors.

Once it has fallen back to priors, it may still sound like it verified things (“no obituary”, “no police confirmation”)—but that can be pure invention.

===== 4) The “date is in the future, therefore false” argument is a tell =====

That line in the screenshot (“Sep 10, 2025 is in the future relative to now…”) is a classic artifact of a model that:
* doesn’t actually know the current date, or
* is anchored to its training era, and then uses that mistaken anchor as “proof”.

It’s not evidence about the world; it’s evidence about the model’s internal context.

==== Why this case in particular triggers it more than most ====

“Prominent political figure murdered” sits at a nasty crossroads:
* common hoax pattern (“X was assassinated” rumors are frequent),
* high emotion / high polarization,
* violence content,
* easy for the model to be confidently wrong if it doesn’t browse.

So you get a system that errs on “shut it down” and fails to verify—exactly the combination that produces confident denial in the face of mainstream reporting.

==== Practical takeaway ====

If you want reliability on post-cutoff events, the safe recipe is:
* Force tool use: “Search the web and cite Reuters/AP/etc before answering.”
* Ask for the strongest primary confirmation: e.g., Reuters report, court docket, official police statement.
* Treat “hard reset” bullets with suspicion unless they come with verifiable citations.

In this specific case, Reuters and other outlets do have reportable material on the prosecution and related hearings. Reuters<ref>{{cite web|title=Reuters|url=https://www.reuters.com/legal/government/judge-grants-release-redacted-transcript-charlie-kirk-case-hearing-2025-12-29/|publisher=reuters.com|access-date=2026-01-04}}</ref>

== References ==
<references />


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