  The capability to reason from text is crucial for real-world NLP
applications. Real-world scenarios often involve incomplete or evolving data.
In response, individuals update their beliefs and understandings accordingly.
However, most existing evaluations assume that language models (LMs) operate
with consistent information. We introduce Belief-R, a new dataset designed to
test LMs' belief revision ability when presented with new evidence. Inspired by
how humans suppress prior inferences, this task assesses LMs within the newly
proposed delta reasoning ($\Delta R$) framework. Belief-R features sequences of
premises designed to simulate scenarios where additional information could
necessitate prior conclusions drawn by LMs. We evaluate $\sim$30 LMs across
diverse prompting strategies and found that LMs generally struggle to
appropriately revise their beliefs in response to new information. Further,
models adept at updating often underperformed in scenarios without necessary
updates, highlighting a critical trade-off. These insights underscore the
importance of improving LMs' adaptiveness to changing information, a step
toward more reliable AI systems.
