PrefPO: Pairwise Preference Prompt Optimization
Prompt engineering is effective but labor-intensive, motivating automated optimization methods. Existing methods typically require labeled datasets, which are often unavailable, and produce verbose, repetitive prompts. We introduce PrefPO, a minimal prompt optimization approach inspired by reinforcement learning from human feedback (RLHF). Its preference-based approach reduces the need for labeled data and hyperparameter tuning-only a starting prompt and natural language criteria are needed. PrefPO uses an LLM discriminator to express pairwise preferences over model outputs and provide feedback to an LLM optimizer, iteratively improving performance. We evaluate PrefPO on 9 BIG-Bench Hard ...
| Authors | Rahul Singhal, Pradyumna Tambwekar, Karime Maamari |
|---|---|
| Primary category | cs.CL |
| Published | 13 March 2026 |
| Last updated at source | 24 March 2026 |
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