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

Matthew Berman tries a prompt that spells messages with sentence initials and compares one simple constraint with GPT-4.

MBMatthew BermanVortrag · Forward Future
Kanal
Matthew Berman
Veröffentlicht
27. März 2024
Video
3:00
Lesezeit
2 Min.
Folien
2
Original
Englisch
Auf YouTube öffnen
Beim Scrollen schrumpft das Video in die rechte untere Ecke und läuft weiter. Untertitel folgen Ihrer Lesesprache.
LesespracheIn 2 Sprachen verfügbar
Diesen Artikel gibt es noch nicht auf Deutsch, daher wird das Original (Englisch) angezeigt.
ZUSAMMENFASSUNG

In 30 Sekunden

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.

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

Das Wichtigste

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

Den bearbeiteten Text nach Kapiteln lesen

Nur das Transkript
01

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.

An acrostic prompt and response
FOLIE 1 / 2An acrostic prompt and responseThe screenshot shows the requested initial-letter pattern and the resulting paragraph.
02

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.

Berman’s own acrostic attempt
FOLIE 2 / 2Berman’s own acrostic attemptA displayed response follows the same kind of initial-letter instruction.
03

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.

Mehr aus diesem Kanal

12 Folien34:21EN
Matthew Berman· 27. Okt. 2026Matthew Berman Examines Anthropic’s IPO Filing, AI Growth and GovernanceMatthew Berman, Sam Altman · Forward FutureThe presenter reviews the S-1’s financial metrics, compute strategy and voting structure while questioning the tension between rapid AI development and safety.AnthropicAI SafetyEnterprise-KI+2
0:26EN
Matthew Berman· 25. Okt. 2026Sam Altman on Human Values and Problem-Solving in an AI WorldSam Altman · OpenAIIn a short interview excerpt, Altman considers the enjoyment of solving hard problems and the human role in interpreting results.Human-AI CollaborationFuture of WorkAI Strategy
2 Folien0:36EN
Matthew Berman· 25. Okt. 2026Kimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.EvalsCoding-AgentenOpen-weight models
2 Folien0:21EN
Matthew Berman· 24. Okt. 2026An AI-Generated Kimi Benchmark Chart Used Invented ScoresMatthew Berman · Forward FutureMatthew Berman asks for a graph of Kimi K3.1 Pro benchmarks, then discovers that the displayed figures are illustrative placeholders.BenchmarksEvalsGenerative media
6 Folien25:50EN
Matthew Berman· 24. Okt. 2026Kimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.Open-weight modelsBenchmarksAI cost & performance+1
2 Folien32:20EN
Matthew Berman· 23. Okt. 2026Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1