做LLM评估的团队终于有了一个公平且高效的基准测试方案——只需微调极少量参数就能剥离格式干扰，直接测出模型真实知识水平。做预训练或模型选型的开发者值得一试，能省下大量后训练成本。
论文指出传统基准测试常因格式要求而低估基础模型的知识水平，因为基础模型缺乏后训练带来的格式遵循能力。作者提出软提示调优（soft-prompt tuning），仅优化10个软提示向量（约7B模型参数的0.0006%），在80步（约640样本）内即可让模型适应基准格式，从而准确反映其真实知识。实验覆盖7个模型和7个数据集，结果显示该方法显著优于零样本和少样本提示，甚至能提升后训练模型的格式合规性。软提示调优后的基础模型性能可更可靠地预测后训练模型的排名，为早期预训练策略选择提供低成本代理。
Soft-Prompt Tuning for Fair and Efficient LLM Benchmark Evaluation
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 soft-prompt tuning, an efficient, fair, and architecture-agnostic model evaluation. By optimizing only 10 soft-prompt vectors (roughly 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. This allows one to fairly compare different base models -- trained with various pre-training recipes -- on benchmarks without the need for full post-training. We evaluated soft-prompt tuning across 7 models and 7 datasets. The results show that (a) soft-prompt tuning saturates format-following within 80 steps (~640 samples) making it highly efficient, (b) soft-prompt tuning significantly outperforms zero- and few-shot prompting, surfacing base model knowledge that standard prompting misses, that (c) even post-trained models can benefit from soft-prompts to maximize format compliance, and that (d) soft-prompted base model performance predicts post-trained model rankings more reliably than zero- and few-shot baselines, offering a low-cost proxy for downstream model quality. Our contributions include (1) metrics which disentangle format-following and knowledge accuracy, (2) a fairer benchmarking protocol of LLM knowledge, and (3) a cost- and memory-effective recipe to identify optimal pre-training strategies early in LLM development.