Google DeepMind's QuestBench benchmark assesses large language models (LLMs) in their ability to address underspecified reasoning tasks by generating critical clarifying questions. The research emphasizes that LLMs often encounter real-world scenarios where tasks lack complete information. To effectively solve these tasks— whether in math, logic, or coding—LLMs must be able to identify missing details and inquire accordingly. The study formalizes this challenge as an underspecified Constraint Satisfaction Problem, differentiating between semantic ambiguity and underspecification to improve LLM responses in practical applications.
DeepMind's QuestBench benchmark evaluates LLMs' ability to identify and articulate crucial questions necessary for solving underspecified reasoning tasks effectively.
The research shows that in real-world applications, LLMs must proactively gather information by asking clarifying questions, ensuring accurate task completion.
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