self-feedback-improves-generation-qualitymechanismsingle papercontested
Prompting a model to generate its own feedback on a draft output and revise accordingly improves quality on open-ended generation tasks (dialogue, code, review-writing) over a single-shot attempt.
Capability: Fixing its own mistakes · Reasoning
Observed on
2023, GPT-3.5/GPT-4 class.
Sources
- Iterative self-feedback and refinement improved outputs across several generation tasks in the original evaluation.
- Re-examines self-feedback-style claims and argues the measured gains often trace back to how the comparison was set up (e.g. oracle-like stopping, or the revision prompt leaking task-specific hints) rather than the self-feedback step itself doing real work.
Suspected axis of disagreement(a guess, not verified)
Likely how rigorously the baseline (no-revision) condition was set up and evaluated, and possibly task family — open-ended generation tasks (where "better" is fuzzier and easier to nudge with a second pass) vs. the more checkable reasoning benchmarks in the contesting paper. I haven't reconciled the two papers' exact experimental setups myself.
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- In UI-to-code generation, where a rendered screenshot gives genuine external feedback, iterative self-critique still drifts below the initial draft over ten rounds, because a code edit changes the rendering non-locally and a fix for one region breaks regions that were already faithful; restricting each round to one typed, scoped repair target and carrying past targets forward as an avoid-list reduces that drift.Generating and editing working code · unreviewed
- Used zero-shot as step-level action critics in long-horizon tool-calling tasks, frontier models are over-pessimistic — flagging a large share of correct actions and pushing the actor into revision loops that lower success below no critic at all — whereas small models fine-tuned on action-level verification rationales flag far fewer valid actions and raise repeated-run reliability.Using the tools it is given · unreviewed
- Without an external, ground-truth signal — a failing test, a compiler error, a verifier's output — a model's own critique of its reasoning is not a reliable improvement signal, and asking it to review and revise a correct answer often turns it into a wrong one.Fixing its own mistakes
- In open-ended editing tasks with no unique correct output (multi-slide design editing), a separate judge model that scores the structural diff between the original and edited artifact against the instruction, and returns its critique verbatim as the next-turn instruction, raises instruction-following over a single pass, but the gain is selective: most tasks pass on the first attempt, and a minority of the ones that enter the loop get worse unless an earlier iteration is restored when the judge's own score declines.Fixing its own mistakes · unreviewed
- On questions whose premises are false or internally contradictory, prompting mid-size instruct models (Qwen2.5-7B, LLaMA-3.1-8B, Gemma3-12B) to reason step by step can lower accuracy below plain answering, because the reasoning chain elaborates from the flawed premise instead of challenging it.Stating false facts confidently · unreviewed
Notes
This is the pair I'd most want to actually dig into further — the disagreement axis above is a first guess, not a verified reconciliation.