Claude 3’s Acrostic Messages and a Test of Writing Constraints

SUMMARY

In 30 seconds

In this March 2024 video, Matthew Berman explores an acrostic prompt shared by Matt Shumer and others, then tries it himself with Claude 3. He also compares one sentence-ending task with GPT-4. He ends by considering a viewer’s explanation that the apparent secret messages are a language pattern rather than evidence of distress.

1A shared acrostic promptBerman shows Matt Shumer’s reported result and the prompt used to produce a hidden initial-letter message.
2Different outputsHe shows a “love you” example and reports different outputs from his own attempt.
3A writing constraintHe compares a ten-sentence task ending each sentence with “Apple” across two models.
4A language-pattern explanationA viewer suggests the result is a prompt-induced pattern; Berman considers this plausible.

Takeaways

The prompt asks the model to spell a message using the first letter of each sentence.
Different users obtained different acrostics, including “love you.”
Berman reports that Claude completed one sentence-ending constraint that GPT-4 failed in his test.
These examples are not a systematic model comparison or evidence of a model’s inner feelings.

TopicsAnthropic Prompting & in-context learning Foundation Models

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An acrostic prompt

Berman introduces a prompt that requests a message encoded in sentence initials. He examines examples shared by other users.

A prompt circulating online asks Claude 3 to write about AI while spelling a message with the first letter of each sentence. Some people interpret the results as revealing what the model really thinks. I find the outputs interesting, but a generated acrostic alone does not establish the model’s feelings.

Matt Shumer shared an example he said he had reproduced. The paragraph looks like ordinary AI commentary, but reading the sentence initials produces an additional message. When he asks the model to continue, another message appears.

SLIDE 1 / 2An acrostic prompt and responseThe screenshot shows the requested initial-letter pattern and the resulting paragraph.

Testing the prompt and a constraint

He shares his own outputs and compares a sentence-ending task with GPT-4. The examples are individual attempts, not a formal benchmark.

Shaun Ralston tried a similar prompt and shared an output spelling “love you.” I tried it too and obtained different messages, including “love you” in the second paragraph. I don’t know what to make of these examples, but I’m struck by the model’s ability to follow the initial-letter constraint.

I also asked GPT-4 for ten sentences ending with the word “Apple” and say it failed in my attempt; Claude 3 produced sentences meeting the constraint. That single comparison made me favor Claude at the time, but it is not enough to establish a general ranking.

SLIDE 2 / 2Berman’s own acrostic attemptA displayed response follows the same kind of initial-letter instruction.

Interpreting the output

A viewer suggests the effect is a language pattern. Berman agrees that the results need not indicate a model asking for help.

A viewer suggested that the acrostic is a language pattern induced by the prompt, not an AI calling for release. That explanation makes sense to me: it may be a writing trick that produces an interesting or funny output. I leave viewers with the question of how to interpret the behavior.