Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs
Michael J. Q. Zhang Eunsol Choi
Resolving ambiguities through interaction is a hallmark of natural language,
and modeling this behavior is a core challenge in crafting AI assistants. In
this work, we study such behavior in LMs by proposing a task-agnostic framework
for resolving ambiguity by asking users clarifying questions. Our framework
breaks down this objective into three subtasks: (1) determining when
clarification is needed, (2) determining what clarifying question to ask, and
(3) responding accurately with the new information gathered through
clarification. We evaluate systems across three NLP applications: question
answering, machine translation and natural language inference. For the first
subtask, we present a novel uncertainty estimation approach, intent-sim, that
determines the utility of querying for clarification by estimating the entropy
over user intents. Our method consistently outperforms existing uncertainty
estimation approaches at identifying predictions that will benefit from
clarification. When only allowed to ask for clarification on 10% of examples,
our system is able to double the performance gains over randomly selecting
examples to clarify. Furthermore, we find that intent-sim is robust,
demonstrating improvements across a wide range of NLP tasks and LMs. Together,
our work lays foundation for studying clarifying interactions with LMs.