Two-Stage Prompt Optimization for Few-Shot Relation Extraction: From Reasoning-Guided Search to Gradient-Guided Refinement
arXiv:2606.29639v1 Announce Type: cross Abstract: Automatic prompt optimization is still underexplored for episodic few-shot relation extraction with smaller language models. We propose a two-stage framework that combines reasoning-based prompt optimization with gradient-based prompt optimization. The first stage can use any reasoning-based optimizer to make broadprompt improvements in natural language. The second stage applies our GradPO, which uses loss and gradient signals to identify high-impact prompt spans and refine them with local edits. Experiments on FS-TACRED and FS-FewRel show that
The continuous evolution of prompt engineering and optimization techniques for language models drives innovation in improving their performance with limited data, making this a timely development.
Sophisticated prompt optimization techniques allow smaller language models to achieve higher performance with fewer examples, democratizing access to advanced AI capabilities and reducing compute reliance.
The ability to automatically refine prompts for few-shot tasks, especially with smaller models, will make AI development more efficient and accessible, potentially accelerating the deployment of specialized AI agents.
- · AI developers using smaller language models
- · Companies with limited data for specific AI tasks
- · Researchers in prompt engineering
- · AI agent developers
- · Companies solely relying on very large, unoptimized models
- · Manual prompt engineers
Improved performance and broader application of smaller language models in few-shot learning scenarios.
Reduced computational costs and energy demands for deploying effective AI solutions, impacting the compute supply chain and energy bottleneck narratives.
Accelerated development and adoption of specialized AI agents for various industries, potentially collapsing certain workflows faster than anticipated.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI