Papers by Haoran Shou
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs (2026.acl-long)
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| Challenge: | Large language models (LLMs) exhibit strong capabilities in reasoning, coding, and complex generation, yet their performance is highly sensitive to prompt design. |
| Approach: | They propose an API-only framework that decomposes a single prompt into semantic factors and updates selected factors while freezing the rest. |
| Outcome: | The proposed framework outperforms strong baselines, improves accuracy by up to +4.29 percentage points on average, and reduces optimization cost by 45–87% tokens on MultiArith while reaching peak validation in 1 step. |