Research map / Large language models / Agents, code and math
Disjunctive Program Synthesis: A Robust Approach to Programming by Example
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| synthesize disjunctive programs with execution-time selection instead of a single ranked program (ablation) | PBE text and web extraction (PROSE DSLs) · Email extraction benchmarks (47 tasks) | % tasks correct from one example: 59.6% -> 93.6% | Helped |
| use disjunctive programs (multiple branches evaluated at execution) (ablation) | PBE extraction programs · Email extraction benchmarks | execution time: 2.9x slowdown | Hurt |
Closest papers by meaning
- Is Programming by Example Solved by LLMs? (NeurIPS 2024 · similarity 0.90)
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- Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages (NeurIPS 2024 · similarity 0.84)
- Training Language Models on Synthetic Edit Sequences Improves Code Synthesis (ICLR 2025 · similarity 0.83)
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