The Self-Correction Illusion: Role Relabeling Gates Explicit Error Flagging in Large Language Models
Kuan-Yen Chen, Fang-Yi Su, Shih-Yen Lin, Bao Li, Jung-Hsien Chiang
Why It Matters
What makes this one worth your time
Understanding and addressing the self-correction limitations of LLMs can improve their reliability and performance in real-world applications.
The paper reveals that LLMs correct errors more effectively when attributed to external roles, not themselves.
Summary
The paper investigates the asymmetry in error correction capabilities of large language models (LLMs), showing that these models correct errors more effectively when the errors are attributed to external sources rather than themselves. The study uses a consistent erroneous claim across different roles and finds that relabeling the claim to an external role significantly increases correction rates. The authors propose a prompt-structure intervention to exploit this artifact without requiring model retraining.
Key contributions
- Demonstrates the role-label artifact affecting error correction in LLMs.
- Proposes a prompt-structure intervention to improve correction rates without retraining.
Notable insights
- Relabeling the source of an error from internal to external significantly boosts correction rates in LLMs.
- A prompt-structure intervention can exploit this artifact without needing model retraining.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.05976v2 Announce Type: replace Abstract: Recent works show that LLM agents struggle to correct errors in their own reasoning traces, despite their ability to correct errors from external sources. We ask whether this reflects a capability deficit or an artifact of the role labeling. To test this, we design a training-free intervention, source-conditioned role relabeling, that keeps the erroneous claim byte-identical and varies only its message role. The claim is presented inside the agent's "", a user message, a tool response, or a system "" block. We test 12 model-domain combinations spanning closed-weight APIs and open-weight models from 70B-class down to smaller families. Relabeling "" to an external role increases the explicit-correction rate by 23 to 93 percentage points, significant in 10 of 12 experimental settings. This suggests that these models' failure to detect a self-generated error is largely an artifact of how the claim is role-labeled in the chat template, rather than a pure cognitive deficit. The most effective role label is domain-dependent: "" dominates in most math experiments, while a user message dominates in logical deduction. Recognizing role-label handling as a key experimental variable in instruction tuning presents a more direct path to closing the self-correction gap.d