TactfulToM: Do LLMs Have the Theory of Mind Ability to Understand White Lies?
This work addresses a gap in assessing LLMs' Theory of Mind for social harmony, though it is incremental as it builds on existing ToM research with a new benchmark.
The paper tackles the problem of evaluating Large Language Models' (LLMs) ability to understand white lies in nuanced social contexts, introducing the TactfulToM benchmark and showing that state-of-the-art models perform substantially below humans on this task.
While recent studies explore Large Language Models' (LLMs) performance on Theory of Mind (ToM) reasoning tasks, research on ToM abilities that require more nuanced social context is limited, such as white lies. We introduce TactfulToM, a novel English benchmark designed to evaluate LLMs' ability to understand white lies within real-life conversations and reason about prosocial motivations behind them, particularly when they are used to spare others' feelings and maintain social harmony. Our benchmark is generated through a multi-stage human-in-the-loop pipeline where LLMs expand manually designed seed stories into conversations to maintain the information asymmetry between participants necessary for authentic white lies. We show that TactfulToM is challenging for state-of-the-art models, which perform substantially below humans, revealing shortcomings in their ability to fully comprehend the ToM reasoning that enables true understanding of white lies.