High-risk domains pose unique challenges that require language models to
provide accurate and safe responses. Despite the great success of large
language models (LLMs), such as ChatGPT and its variants, their performance in
high-risk domains remains unclear. Our study delves into an in-depth analysis
of the performance of instruction-tuned LLMs, focusing on factual accuracy and
safety adherence. To comprehensively assess the capabilities of LLMs, we
conduct experiments on six NLP datasets including question answering and
summarization tasks within two high-risk domains: legal and medical. Further
qualitative analysis highlights the existing limitations inherent in current
LLMs when evaluating in high-risk domains. This underscores the essential
nature of not only improving LLM capabilities but also prioritizing the
refinement of domain-specific metrics, and embracing a more human-centric
approach to enhance safety and factual reliability. Our findings advance the
field toward the concerns of properly evaluating LLMs in high-risk domains,
aiming to steer the adaptability of LLMs in fulfilling societal obligations and
aligning with forthcoming regulations, such as the EU AI Act.

高风险领域中的语言模型性能评估是一个重要问题，本研究对指导调优的语言模型进行深入分析，重点关注事实准确性和安全性，通过在法律和医学两个高风险领域的六个自然语言处理数据集上进行实验，发现目前语言模型存在的局限性，并强调了提高语言模型能力和改进领域特定指标的重要性，以及通过更人性化的方法来增强安全性和事实可靠性，研究结果对于适应高风险领域、履行社会义务并符合即将颁布的欧盟 AI 法案具有重要推动作用。