More Yap Less Meaning: Uncovering Self-Improvement Behavior in SLMs
Recently, language models have made rapid progress across various domains and applications. However, their capability for self-improvement, i.e., whether they are adept at recognising and correcting flaws in their own reasoning, remains dubious. In this study, we address this question by constructing a sufficiency test to rigorously examine the self-correction capabilities of small language models (SLMs). We propose a minimal three-step self-correction pipeline that collects initial SLM answers, prompts the same model to generate hints for its incorrect responses given the ground truth, and feeds the model the same question with its own feedback to refine the initial answer. We evaluate a...
| Authors | Marina Igitkhanian, Erik Arakelyan |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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