MC2: A Minimum-Coverage and Dataset-Agnostic Framework for Compositional Generalization of LLMs on Semantic Parsing (2025.findings-emnlp)
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| Challenge: | Existing research relies on dataset-specific designs or a large number of samples to improve compositional generalization of large language models (LLMs) . |
| Approach: | They propose a minimum-coverage framework that can help LLMs achieve compositional generalization by selecting and organizing samples that satisfy the primitive coverage. |
| Outcome: | The proposed framework can improve compositional generalization on different parsing datasets in the minimum-coverage setting. |
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| Challenge: | Existing approaches to semantic parsing only evaluated on synthetic datasets that are not representative of natural language variation. |
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| Challenge: | Recent research shows that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. |
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| Challenge: | Compositional generalization tests focus on output results without considering sample compositionality, resulting in explainability defects. |
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