  Large language models (LLMs) have demonstrated remarkable capabilities across
various tasks but still face challenges such as hallucinations. One potential
reason for hallucinations is the lack of relevant knowledge or context. Thus, a
promising solution to mitigate this issue involves instructing LLMs to respond
with "I do not know" when a question falls outside their knowledge domain or
the provided context. However, in this work, we observed that LLMs struggle to
admit their lack of knowledge, primarily due to existing instruction datasets
designed to encourage specific answers. To improve large language models'
capability to recognize the boundaries of their knowledge, we propose a novel
approach called uncertainty-sensitive tuning. This method involves two-stage
training designed for uncertainty recognition and prompt-sensitive activation.
In the first stage, we guide the LLM to reject unknown questions. In the second
stage, we recover the decreased performance in QA tasks by incorporating
designed causal instructions. By leveraging this method, we aim to enhance the
model's ability to identify areas of uncertainty. The experimental results
demonstrate that our proposed uncertainty-sensitive tuning method significantly
improves the performance of the Llama2-chat-7B model. Specifically, it achieves
a substantial 34.7% improvement in handling questions involving knowledge gaps
compared to the original model. Moreover, our approach outperforms GPT-4,
exhibiting a 9.4% increase in overall performance. We open-source the model and
code on GitHub.
