Protocol
Give the second check evidence the first answer did not create
Another confident paragraph is not automatically another line of evidence.
When it fits
- You are tempted to verify an AI result by asking the same assistant whether it is sure.
When to avoid it
- Older self-correction studies do not establish the limits of every current model. The external test can also be wrong, so inspect its assumptions.
Why it matters
Choose a check with a different failure route: execute a calculation, inspect the cited passage, compare with a controlled fixture or ask a qualified reviewer to assess the original evidence. Self-correction can help, and training can improve it, but a repeated assurance is not proof that an external check occurred.
Steps
- Identify the exact claim or output that matters.
- Choose a check capable of finding its likely failure independently of the generated explanation.
- Record the observed result and any disagreement instead of asking for reassurance again.
An example
For a generated reconciliation formula, use a small hand-checked dataset with missing, extra and duplicate IDs.
Check your result
The verification report names an actual external observation or executed test.
Keep this limit in mind
- Older self-correction studies do not establish the limits of every current model. The external test can also be wrong, so inspect its assumptions.
Connected ideas
Useful withTest what should stay unchanged when the input changes
Evidence and sources
The cited ICLR study found that intrinsic reasoning self-correction without external feedback could fail or degrade performance in its tested settings.
This finding is model-, training- and task-dependent; it is not evidence that self-correction is universally impossible.
Large Language Models Cannot Self-Correct Reasoning Yet · Abstract
SCoRe reports improved self-correction through dedicated reinforcement-learning training, limiting a universal claim that models cannot self-correct.
Improved self-correction does not establish that a particular result passed an external check.
Training Language Models to Self-Correct via Reinforcement Learning · Abstract