Pundit ClaimsAI claims by Gary Marcus and Ed Zitron, with the evidence

“As we can see, multimodal training is not magically making hallucinations go away.”

Hello, Multimodal Hallucinations, paragraph 11. The full statement is at the source.

Our reading, the sentence that was checked:

Multimodal training is not making hallucinations go away.

Assessment: Supported Evidence as of

Where and when Gary Marcus wrote it

Marcus on AI, Hello, Multimodal Hallucinations, paragraph 11.

Said
Source observed
live page, full text, public
Evidence as of
the cutoff for this assessment
Assessment recorded
assessment marcus-hello-multimodal-hallucinations-d71f3a-a2

We recorded 11 claims from this post. See every claim from this post.

The wording above is the version this site observed on 2026-10-29. The source may have been edited after 2023-10-21. This page does not claim the wording was captured then.

What the claim commits to

Kind
Observation
Deadline, as the speaker wrote it
None stated. This site does not add one.
Deadline state when assessed
No horizon
Deadline state on 2026-10-29
No horizon (the release’s reference date)

From the speaker

  • multimodal training
  • not magically making hallucinations go away
  • As we can see

Our interpretation

  • The plain reading is that adding visual training had not eliminated hallucinations in contemporary multimodal models.
  • The claim does not say that multimodal training offers no improvement or that elimination is forever impossible.
  • I assess persistence after multimodal training, not its isolated causal effect relative to otherwise identical text-only training.
  • For the ongoing state of published evaluations, I use January 1–October 21, 2023, the current research reporting year through publication.

Counts as supported if: Contemporaneous multimodal models exhibit verified hallucinations after training on visual and language inputs.

Counts as contradicted if: Representative contemporary evaluations establish that hallucinations had been eliminated in the relevant multimodal systems, with the alleged failures explained as something else.

Retrospective: these criteria were written after the outcome was public.

Current assessment

Was it true for the period described?

Outcome: Supported

The dated evidence matches what the claim committed to, under the criteria frozen before grading.

Scored on a plain reading. The speaker named no measure for this claim, so it was judged on the reading an informed reader would take, stated below under our interpretation. Both reviews had to find that reading fair.

Evidence as of
Deadline state when assessed
No horizon
Assessment recorded
Policy version
2026-10-28.7

Rationale

OpenAI’s September 25, 2023 GPT-4V system card documented hallucinations during developer testing [e1]. An independent preprint published May 17, 2023 reported objects inconsistent with the supplied images across several vision-language models [e2]. These findings support the observation that multimodal training had not eliminated hallucinations in contemporary models by October 21, 2023 [e1, e2].

Limitations

  • The evidence does not isolate the causal effect of multimodal training or establish that it cannot reduce hallucination rates.
  • The findings concern contemporary models in 2023 and do not establish that future elimination is impossible.
  • The packet does not independently verify the Julia Child kitchen exchange.

Evidence (2 items)

  • Supports the claim: OpenAI,

    prone to hallucinations

    Why it matters here: The release assessment reports hallucinations in GPT-4V itself. Its Be My Eyes discussion also reports reductions in hallucination frequency and severity during testing, supporting improvement without elimination.

    Publisher
    OpenAI
    Kind of source
    Company disclosure: the company describing itself, not an independent finding
    Describes events of
    Public from
  • Supports the claim: arXiv; Yifan Li and colleagues,

    hallucination

    Why it matters here: Independent experiments found object hallucinations across several vision-language models. This supports persistence beyond the single kitchen anecdote.

    Publisher
    arXiv; Yifan Li and colleagues
    Kind of source
    Preprint, not peer reviewed
    Describes events of
    Public from

Where it was said (1)

Said once in the sources this register covers.

  • : Marcus on AI, Hello, Multimodal Hallucinations

    As we can see, multimodal training is not magically making hallucinations go away.

    Published
    2023-10-21
    Source observed
    , live page
    Access
    full text, public

History

Assessment versions

Recorded events

  1. Assessment publishedBy this site

    First assessment published after both reviews and the final-text check passed.

    About assessment marcus-hello-multimodal-hallucinations-d71f3a-a2.