LLMs and In-Context Beliefs

The sources discuss how large language models (LLMs) form internal representations or "beliefs" about users based on conversational interactions, primarily through in-context learning. While LLMs can personalize responses and recall information using different memory architectures like context windows and external storage, they face significant challenges in tracking evolving user states and maintaining consistency. Benchmarks reveal limitations in dynamic adaptation, and ethical issues arise from biases inherited from training data and concerns about privacy and manipulation. Ultimately, the sources suggest current LLM "understanding" is more a sophisticated simulation than genuine comprehension, highlighting the need for more robust memory, bias mitigation, and transparency for ethical and effective user modeling.