ClaimsGary Marcus

“So with LLMs, people do things like put stuff in a system prompt, and they can’t get systems with the system prompts to follow them.”

Gary Marcus, in conversation 2025 Dr. Ben Goertzel's Conversation with Dr. Gary Marcus at the World AI Summit 2025 (SingularityNET) archived copy

Factual AI safety and alignment

People put instructions in LLM system prompts but cannot get the systems to follow them.

Mostly true True in substance, wrong in a detail.

Factual: Statements about the present or the past, checked against the record: did they hold up?

The second judge would not resolve this claim; the verdict shown is the first judge's.

What would count as true or false

Judge as of 2025. The claim means that LLMs commonly fail to follow instructions placed in their system prompts reliably; it does not mean they never comply. True: documented evidence from 2025 (benchmarks of instruction or system-prompt adherence, vendor documentation, research papers, or well-documented incidents) shows that LLMs often fail to follow system-prompt instructions. Mostly true: the failures are well documented, but adherence was high in many settings, so the claim somewhat overstates how often failures happen. Mixed: the evidence is split, with good adherence on simple instructions and frequent failure on complex or conflicting ones. Mostly false: failures exist but are rare edge cases, with adherence generally reliable. False: the evidence shows LLMs reliably follow system prompts.

Set before the research began.

Evidence (4)

  1. E1 A Closer Look at System Prompt Robustness arXiv (academic preprint; authors not Gary Marcus) · 25 November 2023

    “Even in the absence of conflicting instructions, current models still frequently fail to adhere to the initial instructions within the system prompt.”
  2. E2 A Closer Look at System Prompt Robustness arXiv (academic preprint; authors not Gary Marcus) · 25 November 2023

    “the performance of recent LLMs uniformly approaches zero as the number of guardrails increases.”
  3. E3 Control Illusion: The Failure of Instruction Hierarchies in Large Language Models ar5iv (arXiv mirror), arXiv ID 2502.15851 · 8 May 2024

    “Most models show dramatically lower performance (9.6–45.8% average R1) when handling conflicting constraints, compared to their baseline instruction-following capabilities.”
  4. E4 A Closer Look at System Prompt Robustness arXiv (academic preprint; authors not Gary Marcus) · 25 November 2023

    “we see that o3 mini is substantially more robust in following system prompts.”

Reasoning

An independent 2025 paper on system-prompt robustness reports that current models "frequently fail" to follow system-prompt instructions even when nothing conflicts with them (E1). The same paper finds performance approaching zero as the number of guardrails grows (E2). A second independent paper finds sharply lower compliance, 9.6–45.8% on average, when constraints conflict (E3). The first paper also says o3-mini is substantially more robust in following system prompts (E4). So the failures are well documented, though one model, o3-mini, was found substantially more robust than others (E4).

Cite this claim

Gary Marcus, “People put instructions in LLM system prompts but cannot get the systems to follow them.” (said 2025). Verdict: mostly true. Reliability API, claim c_2e6ddf20aadf, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_2e6ddf20aadf

This claim is in dataset release v1.1.2, with the same verdict.

Reply to this claim

For Gary Marcus, or someone speaking for them: a correction or counter-evidence. Replies are read before anything is shown; an approved reply shows your name, role, date, statement and link. Your email address is never shown and is deleted 30 days after the decision (privacy).

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All of them

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