将大模型软提示转化为自然语言描述，提升推理效果
Learning to Translate from Soft to Hard LLM Prompts
- 训练模型将软提示嵌入转换为流畅自然语言
- 转化后的提示在推理中表现优于基线，保留32%原性能
- 有助于理解模型内部机制，适合关注可解释性的研究者
软提示（soft prompting），又称连续提示，是一种高效微调大模型任务的方法。其参数隐含了某种学习过程：能否训练一个模型来解码这一过程，将原始参数“翻译”成自然语言？本文提出一个初步验证：一个能够将软提示嵌入转化为流畅自然语言描述的翻译模型。实验表明，这些描述作为独立提示进行推理时表现超越基线，说明它们不仅是看似合理的内容，更是与任务真正相关的表达。平均而言，这些描述保留了原始软提示32%的性能。我们推测该方法未来可用于模型可解释性、推理优化，或扩展至其他机器学习技术。
原文摘要 · Abstract (English)
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.
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