# Learning to Translate from Soft to Hard LLM Prompts

arXiv: https://arxiv.org/abs/2605.27642 · HTML: https://mlsanity.com/paper/2605.27642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 17 of 17)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Mechanistic Interpretability for AI Safety -- A Review](https://mlsanity.com/paper/2404.14082/) (2024)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery](https://mlsanity.com/paper/2302.03668/) (2023)
- [Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models](https://mlsanity.com/paper/2401.06102/) (2024)
- [Gradient-based Adversarial Attacks against Text Transformers](https://mlsanity.com/paper/2104.13733/) (2021)
- [Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts](https://mlsanity.com/paper/2112.08348/) (2021)
- [Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation](https://mlsanity.com/paper/2205.12647/) (2022)
- [Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?](https://mlsanity.com/paper/2212.10539/) (2022)
- [PromptWizard: Task-Aware Prompt Optimization Framework](https://mlsanity.com/paper/2405.18369/) (2024)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [Prompting a Pretrained Transformer Can Be a Universal Approximator](https://mlsanity.com/paper/2402.14753/) (2024)
- [Eliciting Textual Descriptions from Representations of Continuous Prompts](https://mlsanity.com/paper/2410.11660/) (2024)

## Abstract (arXiv)

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.