Learning to Translate from Soft to Hard LLM Prompts

Soft prompting, also known as continuous prompting, is a parameter-efficient method for tuning LLMs to specific tasks. Like other machine learning techniques, its parameters encode some hidden procedure: is it possible to train a model to decode this procedure---to "translate" raw parameters into natural language? In this work, we present a promising proof-of-concept: a translator model capable of verbalizing soft prompt's learned embeddings into fluent natural language descriptions. We show that these verbalizations when used as standalone prompts for inference surpasses baselines, suggesting that they are not just plausible-sounding descriptions, but genuinely relevant to the task. On average, verbalizations retain a modest but significant 32% of the original soft prompt's performance. We speculate on future directions for how this could be used for interpretability or inference or perhaps even extended to other ML techniques.