Beyond Prompt and Parser Engineering: Soft-Prompt Tuning for Automated LLM Evaluation

Abstract

Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.g., on the model's ability to follow specific formatting requirements. This especially penalizes base models that may know the correct answers but lack the ability -- typically introduced in post-training -- to structure them as instructed. To overcome this, we propose an efficient, automated, and architecture-agnostic method for model evaluation based on soft-prompt tuning. By optimizing only 10 soft-prompt vectors (~0.0006% parameters for a 7B model) over a short tuning period, we adapt models to specific benchmark formats, closing gaps in format-following and ensuring that underlying knowledge is accurately reflected in benchmark scores. We evaluate soft-prompt tuning across 7 models and 11 datasets. Our results show that (a) soft-prompt tuning saturates format-following within 150 steps (~1200 samples) while preserving knowledge accuracy, (b) it closely matches strong evaluation-harness baselines on well-studied benchmarks while requiring no manual benchmark-specific prompt or parser design, (c) it improves evaluation signal in settings where baselines are harder to transfer, including long-context datasets and instruction-tuned variants with different output styles, and (d) it produces cleaner outputs that improve agreement between exact match scoring and independent LLM judges. Overall, we propose a lightweight, automated, and flexible protocol based on limited-budget soft-prompt tuning for evaluating LLM knowledge.