  The task of reading comprehension (RC), often implemented as context-based
question answering (QA), provides a primary means to assess language models'
natural language understanding (NLU) capabilities. Yet, when applied to large
language models (LLMs) with extensive built-in world knowledge, this method can
be deceptive. If the context aligns with the LLMs' internal knowledge, it is
hard to discern whether the models' answers stem from context comprehension or
from LLMs' internal information. Conversely, using data that conflicts with the
models' knowledge creates erroneous trends which distort the results. To
address this issue, we suggest to use RC on imaginary data, based on fictitious
facts and entities. This task is entirely independent of the models' world
knowledge, enabling us to evaluate LLMs' linguistic abilities without the
interference of parametric knowledge. Testing ChatGPT, GPT-4, LLaMA 2 and
Mixtral on such imaginary data, we uncover a class of linguistic phenomena
posing a challenge to current LLMs, involving thinking in terms of alternative,
hypothetical scenarios. While all the models handle simple affirmative and
negative contexts with high accuracy, they are much more prone to error when
dealing with modal and conditional contexts. Crucially, these phenomena also
trigger the LLMs' vulnerability to knowledge-conflicts again. In particular,
while some models prove virtually unaffected by knowledge conflicts in
affirmative and negative contexts, when faced with more semantically involved
modal and conditional environments, they often fail to separate the text from
their internal knowledge.
