Learning to Translate from Soft to Hard LLM Prompts
Quick Take
This study demonstrates that training dedicated soft prompts for natural language translation significantly enhances translation quality, outperforming existing methods like InSPEcT. The research shows that optimized soft prompts on smaller models can be transformed into effective text prompts for larger models, achieving superior performance, even surpassing few-shot learning techniques.
Key Points
- Soft prompt tuning enhances LLM adaptability but lacks interpretability.
- Dedicated soft prompts yield higher translation quality in quantitative and qualitative tests.
- The method outperforms training-free approaches like InSPEcT across multiple datasets.
- Optimized soft prompts from small models can be translated into effective prompts for larger models.
- Results indicate potential for exceeding original soft prompt performance and few-shot learning.
Article Excerpt
From source RSS / original summaryarXiv:2605. 27642v1 Announce Type: new Abstract: Soft prompt tuning is a parameter-efficient method for adapting LLMs to specific tasks, but suffers from a lack of interpretability. Building on recent work on interpreting soft prompts (Ramati et al. , 2024), we explore how training a dedicated soft prompt to natural language translation model can yield higher translation quality.
In particular, in both quantitative and qualitative comparisons on multiple Datasets of Datasets (DoDs), we demonstrate that our translator produces fluent, accurate verbalizations that outperforms existing training-free methods like InSPEcT.
In addition to advancing interpretability, our work suggests a promising downstream application: soft prompts optimized on small, open-source models can be translated into portable text prompts that, when deployed on larger closed-API models, exceed the performance of the original soft prompt and, in some cases, even few-shot learning.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
The reliability of LLM judges for evaluating deep research agents is critically assessed using the REFLECT benchmark.