Aligning Language Models to Explicitly Handle Ambiguity

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.

Properly processing ambiguous inputs is challenging primarily due to the following two hurdles. Firstly, models are not directly trained to explicitly express ambiguity. Even if a model perceives ambiguity, it is challenging to verify the recognition without explicit feedback. The second challenge is that the degree of ambiguity for the query can vary depending on the intrinsic knowledge of the model. Consider the scenario depicted in Figure 1. The initial query is ambiguous as the phrase "national championship" poses various denotations, such as "national tennis championship" or "national golf championship". If a model possesses comprehensive knowledge across the possible denotations, it is plausible for the model to recognize the ambiguity (left). However, if the model’s knowledge is limited to "national tennis championship", it would perceive the query as unambiguous (right). Therefore, it is essential to verify whether the input is deemed ambiguous from the model’s point of view. To overcome these issues, this paper proposes a method to align models to explicitly handle ambiguous queries. Specifically, we design a proxy task that guides the model to self-disambiguate a given query by utilizing its intrinsic knowledge. Then, we quantify the information gain from the disambiguation as an implicit 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 model’s perspective, which are then utilized for alignment