  Large language models (LLMs) have had a huge impact on society due to their
impressive capabilities and vast knowledge of the world. Various applications
and tools have been created that allow users to interact with these models in a
black-box scenario. However, one limitation of this scenario is that users
cannot modify the internal knowledge of the model, and the only way to add or
modify internal knowledge is by explicitly mentioning it to the model during
the current interaction. This learning process is called in-context training,
and it refers to training that is confined to the user's current session or
context. In-context learning has significant applications, but also has
limitations that are seldom studied. In this paper, we present a study that
shows how the model can suffer from interference between information that
continually flows in the context, causing it to forget previously learned
knowledge, which can reduce the model's performance. Along with showing the
problem, we propose an evaluation benchmark based on the bAbI dataset.
