  As research in human-centered NLP advances, there is a growing recognition of
the importance of incorporating human and social factors into NLP models. At
the same time, our NLP systems have become heavily reliant on LLMs, most of
which do not model authors. To build NLP systems that can truly understand
human language, we must better integrate human contexts into LLMs. This brings
to the fore a range of design considerations and challenges in terms of what
human aspects to capture, how to represent them, and what modeling strategies
to pursue. To address these, we advocate for three positions toward creating
large human language models (LHLMs) using concepts from psychological and
behavioral sciences: First, LM training should include the human context.
Second, LHLMs should recognize that people are more than their group(s). Third,
LHLMs should be able to account for the dynamic and temporally-dependent nature
of the human context. We refer to relevant advances and present open challenges
that need to be addressed and their possible solutions in realizing these
goals.
