  Large language models (LLMs) have revolutionized numerous domains with their
impressive performance but still face their challenges. A predominant issue is
the propensity for these models to generate non-existent facts, a concern
termed hallucination. Our research is motivated by the observation that
previous instruction tuning methods force the model to complete a sentence no
matter whether the model knows the knowledge or not. When the question is out
of the parametric knowledge, it will try to make up something and fail to
indicate when it lacks knowledge. In this paper, we present a new approach
called Refusal-Aware Instruction Tuning (R-Tuning). This approach is formalized
by first identifying the disparity in knowledge encompassed by pre-trained
parameters compared to that of instruction tuning data. Then, we construct the
refusal-aware data based on the knowledge intersection, to tune LLMs to refrain
from responding to questions beyond its parametric knowledge. Experimental
results demonstrate R-Tuning effectively improves a model's ability to answer
known questions and refrain from answering unknown questions. Furthermore, when
tested on out-of-domain datasets, the refusal ability was found to be a
meta-skill that could be generalized to other tasks. Further analysis
surprisingly finds that learning the uncertainty results in better calibration
and an improved ability to estimate the uncertainty than uncertainty-based
testing. Our code is available at https://github.com/shizhediao/R-Tuning.
