Papers with prompting-based strategies
Self-Correcting Code Generation Using Small Language Models (2025.findings-emnlp)
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| Challenge: | a recent study has demonstrated that self-correction is a powerful tool for code generation, but whether it is effective for smaller models remains unexplored. |
| Approach: | They propose a method that trains small language models to maintain correct outputs while progressively correcting incorrect outputs as turns proceed. |
| Outcome: | The proposed approach improves the ability of small language models for multi-turn code correction. |
LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers (2023.emnlp-main)
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Theo Olausson, Alex Gu, Ben Lipkin, Cedegao Zhang, Armando Solar-Lezama, Joshua Tenenbaum, Roger Levy
| Challenge: | Logical reasoning is an important task for artificial intelligence, says a new study . many prompting-based strategies to enable large language models fail in subtle and unpredictable ways. |
| Approach: | They propose to reformulate logical reasoning tasks by leveraging large language models . they use a modular neurosymbolic programming approach to translate premises and conclusions from natural language to logic . |
| Outcome: | The proposed approach outperforms open-source models on FOLIO and ProofWriter while showing distinct failure modes. |