Self-Correction is More than Refinement: A Learning Framework for Visual and Language Reasoning Tasks (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown remarkable abilities, but they invariably generate flawed responses. |
| Approach: | They propose a self-correction approach that instructs VLMs to refine their outputs by allowing them to learn from their self-generated self-reference data without external feedback. |
| Outcome: | The proposed approach enables VLMs to learn from their self-generated self-correction data without relying on external feedback, facilitating self-improvement. |
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