  Modern NLP models are becoming better conversational agents than their
predecessors. Recurrent Neural Networks (RNNs) and especially Long-Short Term
Memory (LSTM) features allow the agent to better store and use information
about semantic content, a trend that has become even more pronounced with the
Transformer Models. Large Language Models (LLMs) such as GPT-3 by OpenAI have
become known to be able to construct and follow a narrative, which enables the
system to adopt personas on the go, adapt them and play along in conversational
stories. However, practical experimentation with GPT-3 shows that there is a
recurring problem with these modern NLP systems, namely that they can "get
stuck" in the narrative so that further conversations, prompt executions or
commands become futile. This is here referred to as the "Locked-In Problem" and
is exemplified with an experimental case report, followed by practical and
social concerns that are accompanied with this problem.

Computation and Language;Computers and Society