测量和消除军事大语言模型中的拒绝行为
Measuring and Eliminating Refusals in Military Large Language Models
AI总结：
本研究通过评估军事大语言模型的拒绝行为，提出通过中期训练和端到端后训练实现零拒绝和最大军事任务准确性的方法。
摘要：
军事大语言模型（LLMs）必须在时间紧迫且危险的情况下为战斗员提供准确的信息。然而，目前的LLMs固有的安全行为导致LLM在军事领域拒绝许多合法查询，特别是涉及暴力、恐怖主义或军事技术的查询。我们用于评估拒绝率的黄金基准，由美国陆军和特种部队老兵开发，据我们所知是首个此类数据集。我们展示了31个公开模型和3个军事模型的拒绝和转移率结果。我们观察到拒绝率高达98.2%，转移率范围从0%到21.3%。我们还展示了两个额外的合成数据集的结果，并展示了它们与黄金数据集的相关性。最后，我们使用Heretic库对一个军事调优的gpt-oss-20b模型进行消融实验，显示回答率绝对增加66.5个百分点，但其他军事任务的平均相对减少2%。在结论中，我们呼吁更深入的专业化，包括中期训练和端到端后训练，以实现封闭军事模型的零拒绝和最大军事任务准确性。
英文摘要：
Military Large Language Models (LLMs) must provide accurate information to the warfighter in time-critical and dangerous situations. However, today's LLMs are imbued with safety behaviors that cause the LLM to refuse many legitimate queries in the military domain, particularly those related to violence, terrorism, or military technology. Our gold benchmark for assessing refusal rates, which was developed by veterans of the US Army and special forces, is to our knowledge the first dataset of its kind. We present results for refusal and deflection rates on 31 public models and 3 military models. We observe hard rejection rates as high as 98.2% and soft deflection rates ranging from 0% to 21.3%. We also present results on two additional synthetic datasets and show their correlations with the gold dataset. Finally, we perform abliteration using the Heretic library on a military-tuned gpt-oss-20b model, showing an absolute increase in answer rate of 66.5 points but an average relative decrease of 2% on other military tasks. In our concluding remarks, we argue for deeper specialization, including with mid-training and end-to-end post-training, to achieve zero refusals and maximum military task accuracy for closed military models.
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