Although Large Language Models (LLMs) have shown great potential in Natural
Language Generation, it is still challenging to characterize the uncertainty of
model generations, i.e., when users could trust model outputs. Our research is
derived from the heuristic facts that tokens are created unequally in
reflecting the meaning of generations by auto-regressive LLMs, i.e., some
tokens are more relevant (or representative) than others, yet all the tokens
are equally valued when estimating uncertainty. It is because of the linguistic
redundancy where mostly a few keywords are sufficient to convey the meaning of
a long sentence. We name these inequalities as generative inequalities and
investigate how they affect uncertainty estimation. Our results reveal that
considerable tokens and sentences containing limited semantics are weighted
equally or even heavily when estimating uncertainty. To tackle these biases
posed by generative inequalities, we propose to jointly Shifting Attention to
more Relevant (SAR) components from both the token level and the sentence level
while estimating uncertainty. We conduct experiments over popular
"off-the-shelf" LLMs (e.g., OPT, LLaMA) with model sizes up to 30B and powerful
commercial LLMs (e.g., Davinci from OpenAI), across various free-form
question-answering tasks. Experimental results and detailed demographic
analysis indicate the superior performance of SAR. Code is available at
this https URL

本研究通过研究生成不平等性如何影响不确定性估计，提出了将注意力转移到更相关的组件来处理生成不平等性的方法，通过在各种自由形式的问答任务中进行实验，证明了该方法的优越性。