Learning from Near-Misses: Error-Aware Contrastive Few-Shot Learning for NL2Formula (2026.acl-long)
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| Challenge: | Existing spreadsheet formulas often produce near-miss outputs due to an incorrect function, operator, or reference. |
| Approach: | They propose an abstract syntax tree-based error taxonomy that organizes common error modes by the kind of decision that goes wrong in the parse tree. |
| Outcome: | The proposed framework improves Exact Match (EM) by 6.4 points over supervised fine-tuning and matches self-consistency (SC@5) accuracy. |
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