In recent years, large-scale language models (LLMs) have gained attention for
their impressive text generation capabilities. However, these models often face
the challenge of "hallucination," which undermines their reliability. In this
study, we introduce an uncertainty-aware in-context learning framework to
empower the model to enhance or reject its output in response to uncertainty.
Human-defined methods for estimating uncertainty typically assume that
"uncertainty is lower when the model's response is correct compared to when it
is incorrect." However, setting a precise threshold to distinguish correctness
is challenging. Therefore, we introduce uncertainty information as an
intermediary variable that implicitly influences the model's behavior. Our
innovative uncertainty-aware in-context learning framework involves fine-tuning
the LLM using a calibration dataset. Our aim is to improve the model's
responses by filtering out answers with high uncertainty while considering the
model's knowledge limitations. We evaluate the model's knowledge by examining
multiple responses to the same question for the presence of a correct answer.
When the model lacks relevant knowledge, the response should indicate that the
question cannot be answered. Conversely, when the model has relevant knowledge,
the response should provide the correct answer. Extensive experiments confirm
the effectiveness of our framework, leading to two key findings. First, the
logit output values of the LLM partly reflect inherent uncertainty. Second, our
model autonomously recognizes uncertainty, resulting in improved responses.

通过引入一种不确定性感知的上下文学习框架，我们改进了大规模语言模型的响应质量，并过滤掉具有较高不确定性的答案，从而提高了模型的准确性。