LatentPrompt: Optimizing Promts in Latent Space
This addresses the challenge of improving LLM task performance without manual heuristics, though it appears incremental as it builds on existing prompt optimization techniques.
The paper tackles the problem of optimizing prompts for Large Language Models by introducing LatentPrompt, a model-agnostic framework that uses latent semantic space to automatically generate and refine prompts, resulting in a 3% accuracy increase on the Financial PhraseBank sentiment classification benchmark.
Recent advances have shown that optimizing prompts for Large Language Models (LLMs) can significantly improve task performance, yet many optimization techniques rely on heuristics or manual exploration. We present LatentPrompt, a model-agnostic framework for prompt optimization that leverages latent semantic space to automatically generate, evaluate, and refine candidate prompts without requiring hand-crafted rules. Beginning with a set of seed prompts, our method embeds them in a continuous latent space and systematically explores this space to identify prompts that maximize task-specific performance. In a proof-of-concept study on the Financial PhraseBank sentiment classification benchmark, LatentPrompt increased classification accuracy by approximately 3 percent after a single optimization cycle. The framework is broadly applicable, requiring only black-box access to an LLM and an automatic evaluation metric, making it suitable for diverse domains and tasks.