In spoken languages, utterances are often shaped to be incomplete or vague
for efficiency. This can lead to varying interpretations of the same input,
based on different assumptions about the context. To ensure reliable user-model
interactions in such scenarios, it is crucial for models to adeptly handle the
inherent ambiguity in user queries. However, conversational agents built upon
even the most recent large language models (LLMs) face challenges in processing
ambiguous inputs, primarily due to the following two hurdles: (1) LLMs are not
directly trained to handle inputs that are too ambiguous to be properly
managed; (2) the degree of ambiguity in an input can vary according to the
intrinsic knowledge of the LLMs, which is difficult to investigate. To address
these issues, this paper proposes a method to align LLMs to explicitly handle
ambiguous inputs. Specifically, we introduce a proxy task that guides LLMs to
utilize their intrinsic knowledge to self-disambiguate a given input. We
quantify the information gain from the disambiguation procedure as a measure of
the extent to which the models perceive their inputs as ambiguous. This measure
serves as a cue for selecting samples deemed ambiguous from the models'
perspectives, which are then utilized for alignment. Experimental results from
several question-answering datasets demonstrate that the LLMs fine-tuned with
our approach are capable of handling ambiguous inputs while still performing
competitively on clear questions within the task.

为了处理口语中存在的不完整或模糊的话语，本论文提出了一种方法来使大型语言模型在面对模糊输入时能够处理，并通过实验证明，经过微调的模型在处理模糊输入的同时，在明确的问题上仍然具有竞争力。