  Understanding sentence meanings and updating information states appropriately
across time -- what we call "situational understanding" (SU) -- is a critical
ability for human-like AI agents. SU is essential in particular for chat
models, such as ChatGPT, to enable consistent, coherent, and effective dialogue
between humans and AI. Previous works have identified certain SU limitations in
non-chatbot Large Language models (LLMs), but the extent and causes of these
limitations are not well understood, and capabilities of current chat-based
models in this domain have not been explored. In this work we tackle these
questions, proposing a novel synthetic environment for SU testing which allows
us to do controlled and systematic testing of SU in chat-oriented models,
through assessment of models' ability to track and enumerate environment
states. Our environment also allows for close analysis of dynamics of model
performance, to better understand underlying causes for performance patterns.
We apply our test to ChatGPT, the state-of-the-art chatbot, and find that
despite the fundamental simplicity of the task, the model's performance
reflects an inability to retain correct environment states across time. Our
follow-up analyses suggest that performance degradation is largely because
ChatGPT has non-persistent in-context memory (although it can access the full
dialogue history) and it is susceptible to hallucinated updates -- including
updates that artificially inflate accuracies. Our findings suggest overall that
ChatGPT is not currently equipped for robust tracking of situation states, and
that trust in the impressive dialogue performance of ChatGPT comes with risks.
We release the codebase for reproducing our test environment, as well as all
prompts and API responses from ChatGPT, at
https://github.com/yangalan123/SituationalTesting.
